The Future of Education (According to AI)

In this episode I ask Bard and ChatGPT what the future of education looks like and probe these platforms to get a pulse on what computer science education looks like according to responses from large language models.

• Auto-generated transcript (click to expand)

  AI like chat GPT and Bard have been

  described as autocorrect on steroids but

  before the game sentience and end all of

  humanity as we know it we can use large

  language models like Chachi bati and

  Bard to simulate how a typical person

  within a field might respond to some

  different questions today I'm going to

  ask some questions from Bard and chat

  GPT to kind of like get a general

  understanding of what the discourse is

  around some questions about the future

  of Education as well as some questions

  around Computer Science Education now if

  you're not interested in all the

  questions that I'm about to ask I do

  include time to stamp so you can

  actually jump to a specific question

  that might interest you you can also

  leave a comment in the YouTube

  description if you are interested in

  sharing your own thoughts and what you

  think about the future of Education or

  just computer science education in

  general what did you agree with and what

  did you disagree with in particular now

  the first question that I asked AI was

  what will teaching and learning look

  like 10 years from now now most of the

  responses that are received kind of came

  across as if like a marketing person

  would kind of write something that they

  don't necessarily know a lot about so it

  skims the surface levels of

  understandings of a topic area without

  actually diving deep into it it's

  basically like a blog post that like

  some kind of marketer might write that

  really doesn't do much other than

  improve SEO which is search engine

  optimization so Bard responded to this

  question by saying that there's going to

  be more of a focus on personalized

  learning so students are going to have

  more control over what they're going to

  be able to do over the classroom I hope

  that's the case that's something I've

  been arguing for for years on this

  podcast as well as just in the

  presentations of Publications I do but

  if there was more personalized learning

  fantastic that'd be phenomenal win for

  all the students out there the next

  thing that Bard mentioned was that

  there's going to be more emphasis on

  critical thinking and problem solving

  such as engaging in project-based

  learning and problem-based learning now

  this I definitely see being more of a

  focus because there's going to be a lot

  more questioning in terms of was this

  actually said by this person or was it

  generated by AI is this video of

  somebody like robbing a bank actually

  somebody robbing the bank or is this

  just generated by AI Etc so I see there

  being a lot more going on with critical

  thinking and then problem solving in

  general is just something that's kind of

  very important for a variety of reasons

  I also mentioned that there's going to

  be more collaboration and teamwork as

  well as more use of techno technology

  which was kind of like a yeah well I

  mean duh but the last thing that Bard

  mentioned is that there's gonna be more

  of a focus on global citizenship so this

  was interesting because I was talking

  about how being able to look at things

  from different cultures and perspectives

  not just from like a local sense or from

  like a national sense but globally now I

  do see this as being a potential future

  like with the increase in abilities of

  real-time translations in particular

  we're going to be able to assist with

  more Global discussions so like I'm very

  slowly learning Japanese but there are

  some apps on my phone where I can like

  just speak into it and then it'll

  translate it immediately to somebody in

  Japanese and vice versa they can speak

  Japanese and it'll translate it to me in

  English because of this I do see more of

  a focus on global collaboration and

  communication among students and

  teachers Etc likely going to be

  asynchronous just because of like time

  zone differences and whatnot and like

  when students go to or attend school but

  that is quite frankly really cool like I

  like that idea that we're going to be

  able to have students on one part of the

  world be able to speak into some kind of

  a technology and it's going to translate

  it to other students on another part of

  the world so while most of these

  responses were pretty generic let's see

  what chat GPT has to say so chat chat

  gbt also mentioned that there's going to

  be more of a focus on personalized

  learning but these responses in general

  for all these questions chat GPT had a

  better response than Bard did Bard was

  much more of that like marketing

  response and then chatgpt actually

  sounded like the person sort of knew

  what they were talking about most of the

  time so for example on personalized

  learning it says quote adaptive Learning

  Systems AI tutors and intelligent

  educational software could be utilized

  to create personalized learning paths

  for students catering to their strengths

  weaknesses and interests end quote which

  by the way all these responses I include

  in their entirety in the show notes for

  episode 191 this particular episode so

  make sure you check that out if you want

  to actually see like all of these

  responses from Bard and chat DBT just

  click on the little plus sign and it'll

  expand it so this is a very important

  point so it elaborates on what

  personalized learning looks like I do

  agree that there are some abilities to

  have more adaptive Learning Systems Etc

  that are going to be able to make it so

  students can kind of go at their own

  pace however I've done several episodes

  that talks about a concept known as

  rhizomatic learning for example on

  episode 150 which is titled fostering

  intersectional identities through

  rhizomatic learning as well as episode

  with Catherine bornhurst John Stapleton

  and Katie Henry these episodes and more

  kind of talk about how you can engage in

  individualized learning within group

  spaces so if you're interested in that

  check out those episodes Linked In the

  show notes and the next thing that chat

  GPT mentioned was Blended learning

  environments so this is specifically for

  fostering Global Connections among

  students and Educators this was a really

  interesting idea so it's talking about

  how yeah we're going to have more

  in-person and virtual spaces kind of

  like a hybrid of the two maybe a combo

  of the two but it's specifically in

  connection with like Global Connections

  with other students and teachers that I

  thought was an interesting take on that

  the next thing that Chachi D mentioned

  for this question was an emphasis on

  critical thinking and problem solving

  okay this is basically the same thing

  that Bard mentioned the next one was

  lifelong learning and upskilling this

  one was a really interesting idea so

  specifically talking about how with the

  changes in technology and like jobs just

  constantly evolving in terms of like

  what's available the markets where

  there's needs Etc there's going to be a

  need for an emphasis on lifelong

  learning and upskilling so the ability

  to learn new skills or different skills

  than what you may have learned in school

  there's already a lot of discussions

  about whether or not college is useful

  and whether or not you should get a

  degree or just like jump into a field

  and I do see this as a kind of like

  trickling down into well do we really

  need to focus on like learning this

  particular concept of practice in this

  domain like in computer science when 20

  years from now that domain might not

  even exist as we know it today perhaps

  instead we should focus on the lifelong

  learning and again the upskilling and

  that I think is a really interesting

  idea that Bard did not mention that I do

  think that Educators and the education

  system could really benefit from

  exploring more the next thing that

  Chachi BT mentioned was collaboration

  and project-based learning as well as

  gamification and immersive experiences

  in particular it elaborates and says

  that virtual reality and augmented

  reality are going to become more of a

  norm Within teaching and learning in the

  next 10 years and that is something that

  I agree with and kind of wish that it

  was expanded upon in some of the other

  responses by Bard because this is

  something that is going to potentially

  have a drastic impact on the future of

  learning the next thing that chat gbt

  mentioned was more of a focus on

  data-driven education I'd argue we

  already have a huge Focus or too much of

  a focus if I dare say on data-driven

  education specifically data-driven

  education when it comes to standardized

  tests we spend way too much time on that

  preparing for it taking those tests Etc

  rather than just actually like creating

  learning exploring Etc so I agree that

  unfortunately that is likely going to be

  a big part of the future in the next 10

  years of education but I don't think we

  should focus on that so if you want to

  learn more about that check out some of

  the podcast episodes that have come

  before this there's over 190 of them so

  there's a lot to learn from from the

  different guests and unpacking

  scholarship episodes and the last thing

  that chat GPT mentioned was also just

  like that cultural and Global awareness

  now this was interesting to see these

  responses in particular I really like

  the pro coach of focusing on lifelong

  learning and upskilling out of the

  responses that was probably my favorite

  one to really focus on aside from

  personalized learning which obviously

  I've talked about many many times on the

  other podcast episodes now I followed up

  with both of the AI and asked the

  question how is that different from

  today now again because the large

  language models are basically

  summarizing discourse across a wide

  range of different sources this is kind

  of just giving you a pulse on the

  thoughts around education or the future

  of education so when it comes to

  personalized learning art mentioned

  quote today students typically learn in

  one size fits all classroom setting in

  over their own learning paths and they

  will be able to learn at their own pace

  and in their own way this will be

  facilitated by the use of educational

  Technologies such as adaptive learning

  software and online learning platforms

  end quote I totally agree with that I do

  see that being more of a future but

  again you can do all of that without

  actually having to use technology so

  check out the rhizomatic learning

  podcast episodes Linked In the show

  notes when it came to critical thinking

  and problem solving Bard mentioned that

  many schools focus on basically rote

  learning and memorizing fats Etc and I

  do know that some schools do that

  especially the traditional schools like

  the private schools but when it came to

  collaboration and teamwork Bard

  mentioned that many students work

  individually on projects I personally

  prefer them but I'd argue that the way

  that they're describing collaboration

  and teamwork it's actually not going to

  just be human human collaboration but

  also human to AI collaboration so being

  able to collaborate with AI to kind of

  like come up with a project idea or

  refine some code or to create something

  new Etc I'm predicting that in the

  future of Education there are going to

  be many collaborations with human Nai

  not just human to human that wasn't

  actually mentioned by Bard and then

  again these are just like a lot of

  generic responses and whatnot so when it

  came to the global citizenship there's

  basically saying they're going to learn

  about different cultures and

  perspectives I mean we're kind of

  already doing that in at least most of

  the United States general education

  curriculum like that does happen but I

  would argue that with the use of

  technology and whatnot instead of just

  focusing on learning about different

  cultures and perspectives we're actually

  going to collaborate with communicate

  with directly with the assistance of

  like technology that will allow us to

  translate across different languages Etc

  now when it came to chat gpt's response

  again these were generally better

  responses than barger's responses but

  there wasn't really anything that stood

  out as like oh yeah that's a really

  interesting point I really like that

  um I should talk about that on podcast

  so I'm just going to skip over that but

  again you can actually read the entire

  response from every one of these

  questions that I ask at the show notes

  which is found at jaredeliry.com alright

  so the next question that I asked both

  the AI is what is the role of artificial

  intelligence in teaching and learning so

  Bart mentioned that AI can be used for

  personalized learning as well as

  adaptive learning could be used as a

  virtual tutor you can use it to assess

  something or you can use it for research

  so AI can be used to kind of like come

  up with new teaching methods which is

  something that the AI recommended I'm

  not really sure what that means but it

  sounds interesting so perhaps what I

  could do is just like ask Bard hey what

  are some potential teaching methods that

  can be used or developed through Ai and

  then maybe I can follow up with that on

  a future podcast or something cat GPT

  also mentioned that personalized

  learning was going to be a big focus of

  that intersection of learning and

  artificial intelligence also mentioned

  intelligent tutoring specifically

  pointing out that tutors that are AI can

  be available 24 7 can provide more

  one-on-one feedback that is immediately

  on demand rather than having to wait

  like oh the tutor or teacher is helping

  somebody else in the classroom I just

  have to kind of wait to be able to help

  Etc I mean one of the ways that I kind

  of worked around that as somebody who

  previously worked in the classroom ever

  grade kindergarten through doctoral

  student is when I'd be working in a

  class and I would have like up on the

  board like okay first check like the

  documentation like read through the the

  documents for their coding platform

  using to see if they can answer your

  question then the next two steps are to

  ask some peers so ask somebody next to

  you and then maybe get up ask somebody

  else in the room and then the final step

  is to ask me if I was available if I was

  not available then you keep repeating

  steps one through three which is to

  check the of documentation and then ask

  some peers for help that way it wasn't

  so that like a kid was just like sitting

  there with her hand raised just kind of

  like waiting forever and then eventually

  they were able to actually be able to

  work with me on something because again

  when you have like sometimes I had 35

  kids in a class sometimes when I'm

  working with somebody on a coding

  project like it might be in a lengthy

  discussion where it's like oh well let's

  try this let's try and figure out what

  the bug is let's talk about this blah

  blah and so several minutes might go by

  or even 20 minutes or whatever so it's

  important to have some kind of a system

  in place now again peers might not

  always be able to provide a good answer

  so it might be helpful to have ai to

  kind of be like a tutor I totally

  understand that and interestingly a

  couple weeks ago in episode 188 which is

  titled studying the effect of AI code

  generators on supporting novice Learners

  in introductory programming that

  particular publication actually found

  out that students who learned with AI

  specifically learning how to code

  outperform students who did not learn

  with AI when they didn't use AI on a

  test so when they were just like writing

  out lines of Code by hand without any

  assistance of AI they actually performed

  it if they had originally learned it

  with AI so that is a fascinating finding

  and I'm curious if it can be replicated

  or reproduced in other contexts and

  whatnot so stay tuned folks we'll find

  out now again chat chippy mentioned that

  AI will be helpful for automated grading

  and feedback this can save some time for

  teachers so they can focus on working

  with students one-on-one rather than

  just like focusing on the assignment

  itself however I would argue that a

  teacher learns a lot when they are

  actually going through and Grading

  different things and learning about oh

  here's a misconception that I'm noticing

  and if you were to just hand that over

  to an AI and then AI just assigns some

  kind of a score and gives you feedback

  to the student if you are not the one

  who's actually grading things you're not

  going to be able to notice those

  patterns over time where you're like oh

  Johnny consistently for the past like

  four weeks have had issues with this

  very same thing across several different

  projects Etc so I would argue that it

  might help with grading and feedback but

  it might not necessarily help provide

  context for a teach to understand and

  identify those problems unless they're

  actively looking at the data and going

  oh I'm noticing some patterns right here

  so perhaps if hey I were to kind of

  summarize those patterns and then send

  them to the teacher then maybe they'd be

  able to better assist them one-on-one

  now another thing that the chat GPT

  mentioned was that AI would be helpful

  for natural language processing so this

  was interesting so here's a quote from

  this little paragraph quote AI powered

  chat Bots or virtual assistants can

  engage in conversations with students

  answering questions providing

  explanations and engaging in interactive

  dialogue end quote I am wondering out

  loud if we have chat Bots that are like

  voice activated kind of like Alexa or uh

  text based if that is going to actually

  improve speaking as well as reading

  abilities as students especially if they

  are English language Learners or

  emergent bilingual students I do see

  that as a potential way for students to

  be able to kind of like practice their

  reading practice their Writing Practice

  their speaking Etc and to do so where

  they can actually get feedback because

  like there are many times like when

  you're working with a student when they

  like say something and like the grammar

  is incorrect and you kind of like

  rephrase it back to them without like

  calling them out on it but an AI might

  be able to go like oh the way that you

  phrase this here's how you're actually

  supposed to say it or Etc like if you

  were asking for that from Ai and again

  this is something that can happen in

  real time 24 7 rather than having to

  rely on a tutor who's going to be there

  in person Etc now from what I was

  mentioning earlier with automated

  grading and feedback chat gbt did

  elaborate that data analysis and

  learning analytics would be helpful for

  quote helping identify patterns Trends

  and insights end quote so if it's

  combined with the automated grading and

  feedback and you're actually able to

  look at the patterns over time that I

  think that would be helpful for

  educators even for students to go oh I'm

  noticing that I'm having trouble with

  blah perhaps I should spend more time on

  blah that's something I do with like my

  drumming and whatnot is I will look at

  okay what is going well what are the

  things that are consistently

  inconsistent or consistently not

  sounding good now I need to find some

  different approaches different ways that

  I can actually work on those

  inconsistencies or problems with my own

  playing same thing applies with many

  other things like I've done that with

  martial arts I've done that with Video

  Games Etc trying to find the weaknesses

  of the areas of improvement and then

  finding ways that you can actually

  practice just that thing in isolation to

  improve it over time that could be

  helpful for educators as well as for

  students and I do think AI is going to

  be useful with that now here's another

  interesting one from chat GPT this is on

  intelligent content creation quote AI

  can assist in generating educational

  content such as automated lesson plans

  quizzes or interactive simulations it

  can also provide assistance in creating

  adaptive learning materials that adjust

  to individual student needs end quote

  that is really interesting and in fact

  we put that to the test a little bit

  later so I actually do ask both the AI

  to create some lesson plans and there's

  a bit of controversy in there that we'll

  talk about when we get to that but you

  can actually jump to that by clicking

  the description as I do include

  timestamps to each one of the questions

  that I ask and then the last thing that

  chat GPT mentioned is that virtual

  reality and augmented reality can be

  used to kind of like simulate real world

  scenarios experiences Etc now one

  interesting thing that chat GPT ends

  with is the following little paragraph

  quote it's important to note that while

  AI has the potential to enhance teaching

  and learning it is not intended to

  replace human Educators rather AI can

  complement and support teachers by

  automating routine tasks providing

  insights and offering personalized

  assistance thereby enabling Educators to

  focus on individualized instruction

  mentoring and fostering human Connection

  in the classroom end quote that is a

  really interesting thing that it adds at

  the very end of this now I have seen

  some business people in particular kind

  of talk about how AI is going to replace

  Educators and there's not going to be

  needs for them I completely disagree

  with that I know I'm a bit biased

  because like again I've worked with

  evergrade kindergarten through doctoral

  student but I don't see Educators going

  away or that Personal Touch I do see it

  being used in collaboration or being

  able to assist but having somebody there

  in person is just so much better because

  you're actually able to get to know the

  student as an individual at least if

  you're teaching that way as opposed to

  teaching to the group or to like the

  group mean or medium but I don't know

  maybe 10 years from now I'll be like uh

  actually Jared computers have completely

  replaced teachers because as I mentioned

  in that other podcast episode I did find

  that AI that was used in collaboration

  with learning actually performed better

  than students who did not learn with the

  collaboration of AI but again no no

  we'll see now the next question that I

  ask about the AI is why are teachers

  leaving the field and what can be done

  to prevent that so Bard mentioned that

  the average teacher salary in the United

  States is 61 730 which is considered to

  be low pay and quite frankly that was

  more than I was making as somebody with

  a doctorate 10 years of experience and

  they gave me 44

  classroom

  rolling in that money they also

  mentioned that there's a heavy workload

  there's a lack of support there's

  unreleased students and parent

  involvement is not happening I would

  argue that there's sometimes too much

  involvement from parents who think they

  know everything about their student and

  don't see all the bad things the

  students are doing behind their back but

  I also understand that some of the

  schools that I've worked at did not have

  enough parent involvement at home

  especially if the parents are having to

  work multiple jobs now again much like a

  marketing person trying to respond for

  an expert Bard mentions well we can fix

  these by increasing teacher pay reducing

  teacher workload by reducing the class

  sizes allowing for more planning time

  allowing for more professional

  development we can provide more support

  through the axis of more technology

  smaller class sizes we can also develop

  positive relationships with students

  quote teachers can develop positive

  relationships with students by being

  fair and consistent showing respect and

  being approachable end quote I would

  argue with somebody again who's worked

  with ever-grade kindergarten through

  doctoral students that students are the

  least of the problems that I had in

  education yeah of course there's going

  to be some outbursts or some students

  who are going to fight each other every

  now and then but then you just step in

  you knock one of them out and then it'll

  never happen again because no one will

  want to mess with with you that was a

  joke but this idea that like

  education is going to be so much better

  if like you just approach students with

  respect and be consistent like teachers

  aren't leaving the field because

  students are unruly there are so many

  like issues in education educational

  policy so many influences from parents

  from policy makers from community

  members Etc who are just making it so

  that it's unbearable to be in the

  classroom not to mention you know it's

  not safe to be in the classroom because

  random people are coming into the school

  or even sometimes students with weapons

  like it's a scary time to be an educator

  and again you're not getting paid much

  so I get the sentiment but if that's the

  general discourse that that's kind of

  blaming the teachers which again I would

  argue that is all the external

  influences on the educator not the

  educator itself that is the problem for

  the majority of the time yeah I've

  worked with some teachers who like

  clearly didn't want to be there but I

  mean given all the stresses the low pay

  etc etc like I get why you wouldn't want

  to put a lot of effort into it again

  somebody with a doctorate and 10 years

  of experience I made forty four thousand

  one hundred and eighty six dollars I've

  presented over a hundred times I have

  over a dozen Publications I've won

  multiple International teaching and

  research Awards and I was making less

  than fifty thousand dollars a year and

  I've seen some people on social media be

  like yeah well if you want to make more

  then how about you get more degrees and

  how about you just like perform well I

  did and I still made less than fifty

  thousand dollars a year but I'll end

  that rant right there now another thing

  that Bard mentioned is that we can

  encourage more parental involvement and

  I would argue that in lower income

  schools again parents don't necessarily

  have time for that all right so let's

  take a look at what chat gbt said so

  it's very similar so it's saying that

  teachers are leaving the field because

  of low salaries heavy workload and

  stress a lack of support and

  professional development again they

  mentioned classroom management

  challenges that there's diverse student

  needs disruptive behaviors a lack of

  resources so it was a little bit blaming

  the Educators but also expanding it

  saying hey there's a lack of resources

  and there's so many things you have to

  consider when teaching like if there's

  IEPs or 504 plans Etc and I totally get

  that so this was a little bit more

  nuanced than Bard's explanation was so I

  like this response a little bit better

  and the last thing that chat GPT

  mentioned is that policy changes and

  standardized testings are some of the

  reasons why teachers leaving and I agree

  a lot of what you focus on in the

  classroom is not necessarily students

  which is kind of a shame because that's

  why like I went into education into the

  field is to be able to help other kids

  but you have so many outside factors

  that you have to consider I have to

  prepare you for the test rather than you

  know just focusing on things you

  actually want to learn and again

  developing lifelong learning and

  upscaling Etc now chat GPT mentions that

  we can address this by having

  competitive compensation we can have a

  more supportive work environment reduce

  the administrative burden such as like

  administrative tasks and paperwork which

  yeah that would be nice I had to do

  lunch duty playground Duty Bus duty all

  sorts of other stuff which honestly is

  such a waste of my time again given my

  background like when I left the

  classroom I went to a non-profit and

  made curricular content and professional

  development that made that organization

  several million dollars because of the

  content that I personally made or led

  the team of and so instead of making

  several million dollars worth of content

  I was monitoring the bus and making sure

  that kids weren't jumping off the

  playground Etc like that is just kind of

  a waste of time and you could hire

  somebody at minimum wage to do that

  rather than taking away from my time to

  be able to create educational content

  and resources that help students but

  that is my own little rant on that

  another thing that chat CPT mentions is

  that you can focus on professional

  development and growth now this is

  something that I mentioned in other

  episodes is that there is very little

  money and time set aside for

  professional development and growth that

  usually occurs like on those Wednesday

  half days where like students get out

  early and teachers end up staying way

  later than they should because they have

  like several hours of meetings after

  school ends if you are lucky enough to

  get professional development during the

  school day that means that you're going

  to have to hire several if not dozens

  and dozens of substitute teachers during

  that day to replace that time when the

  teachers are leaving in the classroom

  and now somebody's got to be in there to

  like support the students but it's not

  going to be as high quality I mean I've

  had so many Subs who literally just like

  sat on their phone in there it didn't

  matter what kind of lesson plan you gave

  them they would literally do nothing in

  the class be like I don't know today's a

  game day have fun so while you might be

  able to have the funds to pay for the

  supports that you would need to be able

  to do the professional development it's

  going to have a negative impact on

  students so like there's really no win

  there when you're doing this

  unfortunately another thing that chat

  gbt mentions is that we need more

  teacher voice and collaboration

  especially when it comes to like

  policies and decision making I agree

  with that sentiment so there there is

  not enough like discussion with policy

  makers like I had a policy maker come

  into a district that I worked in and

  this person literally to the minute had

  their their entire like hour-long thing

  planned out they had I'm gonna spend

  three minutes in this classroom and then

  we're going to spend one minute walking

  to the next classroom another five

  minutes in this other classroom blah

  blah blah blah blah now I didn't

  actually teach at the school but I was

  asked to come into that school and act

  like I was a teacher there because again

  I was teaching in a completely different

  School in the same district they asked

  me to come over there simulate what my

  classroom looked like in that school I

  taught the kids like the week before for

  several days trying to help them I get

  caught up to what my students were doing

  in my actual classes students then were

  in there showcasing the projects that

  they worked on in their class and then

  they had a list of pre-approved

  questions that students could ask this

  policy maker so a student would be like

  reading from a script basically saying

  what do you think about the future of

  education and then the policy maker

  would say their little buzz line for a

  moment and then within five minutes they

  were gone and then after that I never

  saw those students again because I went

  back to my actual School my actual

  classes and those kids just kind of

  continued to do their own thing so

  policy makers who actually come into the

  classroom sometimes and at least this

  case come into an artificial classroom

  and then policy makers walk away from

  this going yeah this school was amazing

  like look how great this was these kids

  like asked such well-informed questions

  that you know were pre-approved by my PR

  team and look at all the fancy stuff

  they have on the walls that they

  certainly didn't put up the week before

  I actually arrived there so not only

  should like policymakers actually walk

  into a class classroom to see what it's

  like on a normal day and not just these

  like scripted PR stunts but they should

  actually like go in and figure out what

  it's like to be a substitute for a day

  or to teach for a day because you know

  what it's a lot harder than the looks

  there's an idea called the

  apprenticeship of observation which is

  basically a fancy way of saying hey

  you've been in school for maybe 13 plus

  years if you were in K-12 and you have

  observed somebody you've been

  apprenticed by all these Educators that

  you have had you've looked at it and

  gone I know how to teach I know what's

  going on even though you've never asked

  the teacher what are you thinking why

  are you doing it this way and not this

  way how is it that you would just

  adjusted this lesson based off of what

  students responded with blah blah blah

  blah you've observed but you don't

  understand the thinking behind it so

  this creates an issue where policy

  makers think they know what they're

  talking about because they've observed

  people teaching for decades and then

  they go oh I know how to do that as well

  I know it's best here's what worked for

  me but what they don't see is somebody

  like myself who has literally worked

  with 1000 of students individually can

  go well no the way that I'm teaching you

  policymaker is going to be different

  than I teach the person next to you

  based off of their own needs their own

  understandings their perspectives Etc so

  long rant to basically say yeah there

  needs to be way more teacher voice and

  collaboration with people who have all

  this external influence on what goes on

  inside of the classroom because quite

  frankly those people don't know what

  they're talking about because they

  haven't actually sat down and spoken

  with Educators and they haven't actually

  been in a classroom and experienced

  things from a teacher's perspective I'll

  end their aunt there if you want to hear

  more on these perspectives check out the

  my website now the last thing that chat

  GPT mentioned is teacher well-being and

  mental health support this is something

  that I have over emphasized on this

  particular podcast each year I release

  an episode that is specifically on

  burnout that takes all of the responses

  from guests and says here's how these

  individuals try and prevent burnout

  working in the field of Education or as

  a researcher Etc so make sure you check

  out the links in the show notes for

  those episodes if that is interesting to

  you now the next question I ask both the

  AI is how much should a public

  elementary school computer science

  educator be paid in Arizona with 10

  years of experience and a Doctorate in

  Education now Bard laughingly said that

  the median salary for elementary school

  teachers is 56 590 in 2020 and that

  somebody with those experiences that I

  just described should be making a salary

  of seventy five thousand dollars or more

  now again I have my contract here that

  said that in the 2016-2017 school year I

  was making 44

  it's described on there today in today's

  dollars I would be making if I had zero

  experience forty nine thousand dollars

  with a PhD in education or a maximum

  again maximum of sixty one thousand

  dollars with the 10 plus years of

  experience that I have because they do

  not count above 10 years but they do

  account for the PHD so I would be making

  sixty one thousand dollars if I was

  still in the classroom but Bard is

  suggesting that I should be making

  seventy five thousand dollars or more

  because of that experience in education

  and as a researcher etcbt on the other

  hand had a response that basically said

  hey I don't really have access to

  real-time data on salary so here are

  some ways that you can figure that out

  on your own you can check out the

  Department of Education you can go to

  school district websites or you can go

  to a teachers union and I agree that is

  how I'm able to find the the salary

  information but it really kind of makes

  me laugh it's just like so far off how

  much the educator will be making in the

  classroom if I was making seventy five

  thousand dollars a year as an educator

  that would have been sweet but again I

  was making Thirty one thousand dollars

  lesser than that now I took the same

  question and I applied it to a private

  elementary school educator so like in a

  charter school and so for chat GPT again

  it just basically said hey I can't

  really figure that out but here are some

  ways that you can do that but Bard said

  that the salary range can be from fifty

  thousand to a hundred thousand dollars

  or more and that the median salary for a

  elementary school teacher in private

  schools was 72 000 now this is

  fascinating because a lot of charter

  schools a lot of private schools you

  don't actually need to be certified to

  become an educator in the school you

  could literally have never taught a day

  in your life never have a single degree

  in education and you could just say hey

  I have like a master's degree in

  computer science I'm going to Now teach

  your computer science class and then be

  like cool here's 72 000 or whatever so

  you could potentially make more with

  less experience by working in a charter

  or private school obviously mileage may

  vary Etc depending on what school you're

  working at let me know in the comments

  on YouTube If you uh agree or disagree

  with that that private school Educators

  with potentially less experience should

  be getting paid more or less than public

  school teachers who have more experience

  in background in education alright so

  those were some of the questions that

  were like broadly talking about

  education now I want to narrow down to

  talk about well Computer Science

  Education in particular because this

  podcast does focus on Computer Science

  Education all right so the question that

  I ask is what are the top five

  pedagogies for teaching computer science

  Bard responded with project-based

  learning problem-based learning

  approaches to computational thinking

  which is apparently some kind of a

  framework I've literally never heard of

  this so it may be a hallucination and

  just like coming up with computational

  thinking and turning it into a framework

  I even searched for it and I couldn't

  find stuff on it just generic stuff

  about computational thinking the fourth

  amount was inductive inquiry and then

  the last one was experiential learning

  now if you want to like again see what

  each of these are I do include them in

  the show notes oddly enough Bard

  responded with like here are some more

  tips like on how to teach computer

  science so one is to make things more

  relevant one is to use more visuals make

  it fun provide feedback and celebrate

  successes again it just just reads as if

  it was written by somebody who's like on

  a marketing team who's never taught in a

  day in their life but has read some

  educational blogs about how to teach

  computer science like if you look at the

  make it fun it just has two sentences

  and it says computer science should be

  fun an exclamation point this can be

  done by using games puzzles and other

  activities okay and now I'm a phenomenal

  educator thanks for that advice chat GPT

  on the other hand had very similar

  things these answers were more detailed

  and less of a blog post from a marketing

  team but again it had some of the

  similar ideas just with like more Nuance

  to them so so the suggestions are to use

  project-based learning inquiry-based

  learning Collaborative Learning flipped

  classrooms and game-based learning but

  one of the things that I really

  appreciate at the end of this is the

  following quote it's important to note

  that these pedagogical approaches can be

  combined or adapted based on the

  specific needs of students the learning

  objectives and the available resources

  effective computer science instruction

  often involves a blend of these

  pedagogies along with integration of

  Technology tools formative assessment

  strategies and differentiation to meet

  the needs of Learners end quote that was

  actually a really good solid like

  explanation of this I've talked about

  this multiple times on the podcast

  episodes that have come before this that

  a multi-perspectable approach to

  education is the way that I highly

  recommend Educators approach things

  approach things from many different

  perspectives because what works for one

  student or group of students might not

  work for another student or groups of

  students so if you can approach things

  from many different ways have many

  different paths Etc then this can be

  very helpful so I'm actually happy to

  see that chat gbt mentioned that in the

  response on here for the top five

  pedagogies for teaching computer science

  now I followed up with each of the AI

  and asked can you elaborate on how each

  of these pedagogies elicit will be

  beneficial for learning asking this a

  Bard made it so that there were some

  better answers for these different

  approaches and kind of focused more on

  how it actually would help students the

  chap gbt gave four reasons for each of

  the five pedagogies for why that it

  would be useful for the students so chat

  CPT again gave a significantly better

  response to this and again if you want

  to read what these like responses are

  then check them out in the show notes

  now one of the things is not discussed

  enough quite frankly are the downsides

  for using different pedagoges so I asked

  both Bard and chat gbt what are the

  downsides for each of the pedagogies

  that they listed now Bard provided some

  of the downsides and they also actually

  provided some suggestions for how to

  mitigate each of these downsides which

  is something that I didn't ask for and

  then chatgpt had some more nuances for

  their answers than Bard did and

  basically answered the questions now

  here's like an example of how it

  responded to this so for example in

  Collaborative Learning quote unequal

  conditions in Collaborative Learning

  some students May contribute more or

  less than others leading to imbalances

  in workload and potential frustration

  among group members conflict resolution

  collaborative settings may present

  challenges related to conflicts or

  disagreements among students teachers

  must facilitate effective communication

  conflict resolution and Equitable

  participation individual accountability

  ensuring that each student is

  accountable for their learning and

  actively engaged in group activities can

  be challenging in Collaborative Learning

  environments end quote and again this is

  like all things that I was kind of

  ranting about earlier with Collaborative

  Learning so that I felt like was

  actually a pretty solid response as well

  as like some of the discussions on

  game-based learning like having done

  Publications and research on like video

  games and learning and whatnot like I

  agree that game-based learning can be

  really cool but there are some

  significant downsides that are often not

  discussed so chat cpt's response to

  game-based learning and the downsides of

  it was actually like pretty nuanced was

  was pretty good which again you can find

  in the show notes so the next question

  I'm asked both the AI was how could

  teachers make computer science classes

  more Equitable so Bart mentioned that we

  could use a variety of teaching methods

  we could provide individualized support

  we could use culturally relevant

  examples create a welcoming environment

  and be a role model they also provided

  some additional tips for it which was

  kind of like weird how it was phrased

  but they said to be aware of your own

  biases create a space for students to

  share their experiences provide

  opportunities for students to

  collaborate and celebrate students

  successes by making them I like feel

  like they belong now let's go back to

  the beware of your own biases this was

  interesting quote teachers should be

  aware of their own biases and how they

  might be affecting their teaching for

  example teachers who are not familiar

  with coding might be more likely to call

  on students who they think are good at

  coding even if those students are not

  from underrepresented groups end quote

  so this was actually like a pretty

  decent response like I'm surprised that

  it did mention that and like I actually

  would advise that other Educators become

  more aware of their own biases is

  something that I actively try and do but

  again it's one of those things where

  it's like what 10 years from now am I

  going to be appalled that I'm I believe

  or think now and obviously I don't know

  that so stay tuned folks chatgpt had

  again some more nuances and whatnot but

  the different recommendations that they

  get gave for making classes more

  Equitable was to have an inclusive

  curriculum and instruction addressing

  stereotypes and bias improving access

  and infrastructure having a more

  supportive classroom environment and

  Community engagement and Outreach all

  these have like three to four different

  suggestions on how you can do that for

  each one of these so for example on your

  inclusive curriculum and instruction

  chat GPT mentioned quote select diverse

  and inclusive learning resources

  materials and examples that represent a

  range of cultural perspectives and

  backgrounds incorporate projects coding

  exercises and problem solving tasks that

  relate to students's interests and

  experiences ensuring relevance and

  engagement for all Learners use multiple

  instructional strategies and modalities

  to accommodate diverse learning styles

  and preferences provide clear and

  structured instructions to support

  students who may require additional

  guidance or clarification end quote all

  of those I agree with I like honestly

  I'm kind of surprised at how well the

  chat GPT in particular can provide a

  pretty decent answer for some questions

  like honestly a lot of the responses

  that I'm getting here could have been

  found in like educational journals for

  like practitioners it's not quite at the

  Nuance yet of an academic based

  publication like for researchers and

  Scholars and whatnot but I imagine in

  that depending on the prompts that you

  give it and the writing styles that you

  ask it to write in it might be able to

  elaborate on this like hey can you write

  this from like a graduate student's

  perspective as opposed to like

  undergraduate's perspective now I wanted

  to get into some more provocative

  questions so I ask a question why should

  all students learn computer science

  because I've talked about on this

  podcast even though I love computer

  science Computer Science Education I

  don't think everyone should learn any

  subject area same thing with like music

  my bachelor's Masters and PhD are all in

  music education but I don't think

  everyone should do music or music

  education I think it depends on like

  what your interests are and what you

  want to do but I also think that there

  shouldn't be required classes in general

  so I wanted to see what would AI kind of

  summarize as why people think students

  should learn computer science now this

  was very like markety very buzzwordy so

  it's talking about computer science as a

  foundational skill it can be a creative

  field that it's a problem-solving field

  it's collaborative and it's fun but it

  also said computer science can solve

  quote real world problems such as

  climate change or poverty end quote I'm

  looking forward to the day when computer

  science solves climate change and

  poverty but I'm not holding my breath on

  that now Chachi PT on the other hand

  said that students should learn computer

  science because of digital literacy and

  essential skills problem solving and

  critical thinking there are career

  opportunities you can engage in

  creativity and Innovation because of

  computational thinking and algorithmic

  mindsets because of empowering digital

  citizenship interdisciplinary

  connections technological literacy and

  adaptability as well as addressing

  equity and access all of these in my

  opinion were very buzzwordy and didn't

  really have much substance to the

  thoughts behind them so for example A

  Bard's response to why computer science

  is a foundational skill it says quote

  computer science is a foundational skill

  that is essential for success in today's

  world computers are used in almost every

  aspect of Our Lives from work to school

  to entertainment by learning computer

  science students can develop the skills

  they need to be successful in the 21st

  century end quote that was a whole lot

  of nothing it sounds great like some

  kind of like a CEO executive or whatever

  is going to be like yeah this is why we

  should learn this thing but if you

  substitute the word computer science

  with technology or computer or phone or

  whatever it works just as well like

  nothing about computer science Concepts

  and practices was actually mentioned in

  there and Chachi BT's response it does

  say that you can figure out how things

  work and to have a better understanding

  of like digital tools and whatnot so

  that's at least some nuance and it's not

  just talking about computers at large

  but having like been on some Grant

  reviews and whatnot for like the

  Department of Education for several

  million dollars sometimes I've seen some

  of the applications where people will

  talk about computer science as if it's

  just using a computer like conflating

  the idea that learning how to use

  PowerPoint is going to help you learn

  and understand computer science those

  are two very different things in my

  opinion so again this was very buzzwordy

  and didn't really have much substance to

  it now trying to probe it a little bit

  more because I like to probe the general

  discourse around computer science and

  why it's like the most important thing

  in the world for some people and why

  everyone in the world apparently needs

  to learn it I asked the question for the

  AI should someone learn computer science

  if they are not going to be a

  professional computer scientist and so

  both the AI said yes but they just

  listed off basically saying some of the

  same stuff like Bard was like it's a

  foundational skill you need to learn how

  to use computers it's a creative thing

  it's problem solving it's collaborative

  and it's fun then it ended with this

  quote quote even if you don't want to be

  professional computer scientist learning

  computer science can still help you in

  your career many jobs now require some

  level of computer skills and learning

  computer science can give you a

  Competitive Edge additionally computer

  science can help you develop critical

  thinking problem solving and

  communication skills that are valuable

  in any field if you're interested in

  learning more about computer science

  there are many resources available

  online and in libraries you can also

  take classes at local community college

  or university with a little effort you

  can learn the basics of computer science

  and start using these skills to improve

  your life end quote again that was a

  nothing Burger there was like really no

  substance to that but that right there

  kind of summarizes like the general

  discourse that I I often see discussed

  with like technology in education

  computer science Educators take it one

  step further and actually talk about

  okay well instead of just using devices

  which is basically all Bard was talking

  about you can learn how to program those

  devices which is useful for upcoming

  jobs and that's mainly the argument that

  is used I like to focus on the other

  side of things balance things out a

  little bit more and say yeah you can

  also use it for leisure like I propose

  to my wife by modding the video game

  Minecraft I added our dogs to it I added

  custom purple armor because that's her

  favorite color all sorts of like fun

  things into it I'll include a link in

  the show notes if you want to see the

  video of some of the stuff that I added

  into it and it was great I loved it I

  did it for fun well and you know to get

  married but then there are other like

  games and like tools that I made for

  myself and none of it was made for money

  or monetary gain Etc so yeah you can

  focus on that side of things the career

  side of things but also you can focus on

  well if you're not gonna like do this as

  a profession you can still do this for

  fun you can engage in modding video

  games if you want to just like do that

  in your leisure or just create apps that

  would be helpful for you in your

  everyday life again chat CPT basically

  responded with yes it's important and

  here are some reasons why and they were

  like fairly generic reasons again you

  can engage in problem solving yeah well

  is computer science the best way to

  engage in problem solving that would be

  relevant to that individual learner

  who's not actually interested in

  computer science maybe maybe not though

  there are other ways you can engage in

  problem solving and all the other things

  that are listed on here without actually

  using computers so I followed up with

  both the AI and asked okay well what

  prevents students from being interested

  in computer science I responded by

  saying that there are perceptions that

  computer science can be difficult that

  there's a lack of female role models or

  lack of diversity and that there are

  negative stereotypes that it's like

  boring in a solitary field Etc so

  provide some suggestions on things that

  can be done to address those and you can

  check those out in the show notes now

  chat GPT had a several more examples of

  what prevents students from being

  interested in computer science so

  mentions that there's a lack of

  awareness unlike the potential careers

  you can have in compete with computer

  science as well as like the different

  concepts and it's real world

  applications of computer science also

  talks about different stereotypes and

  perceived incompatibility limited role

  models and representation inadequate

  preparation perceived Notions of

  difficulty a lack of engaging in

  relevant curriculum there are gender and

  cultural biases and then there's a

  limited access to resources and

  opportunities such as like technology

  quality instruction extracurricular

  resources

  etc etc I do agree with all these These

  are are different reasons why people

  might not be interested in computer

  science but I'd also like to add that

  some students are not interested in

  computer science because they're more

  interested in other areas so for example

  if you are a computer science educator

  why did you go into this field as

  opposed to other fields that other

  people went into I went into music

  education and now have been in computer

  science education for the better part of

  the last decade so I'm technically in

  two Fields simultaneously but I've also

  as of like January have been working on

  gaming stuff as a streamer as a content

  creator and whatnot for my YouTube

  channel Channel and my website so I

  technically have had like three distinct

  careers but that's three out of the

  millions of other careers and domains I

  could have gone into so same thing for

  this like just because a student is not

  interested in computer science it

  doesn't necessarily mean that they have

  to be in my opinion it's okay if they're

  interested in other areas like cool you

  want to be a physical therapist then be

  the best physical therapist you can be

  cool if you want to be a dentist then be

  the best dentist you can be if you want

  to be a therapist be the best therapist

  you can be and computer science may or

  may not intersect with that but it's

  okay if it doesn't in my opinion and you

  can disagree with me in the comment

  section on YouTube now the next question

  that I ask AI is how can teachers learn

  how to teach computer science classes if

  they don't have a background in computer

  science I was curious honestly if like

  the responses from Bard and chat GPT

  would kind of align with some of the

  different approaches that I use because

  I use several different perspectives

  again the multi-perspective approach try

  and look at learning from many different

  angles and whatnot so Bard mentions you

  can take a computer science class you

  can read computer science books and

  articles you can attend computer science

  conferences and workshops I'd argue that

  csda is a fantastic way to do that you

  can also connect with other teachers who

  are teaching computer science you can

  use online resources like Khan Academy

  code.org and scratch which there are a

  lot of podcasts that are dedicated to

  talking about scratch so check those out

  in the show notes and then it provides

  some suggestions for learning how to

  teach it so start small use Hands-On

  activities and be patient I agree with

  all those you can start small like focus

  on a specific project that is

  interesting to you you don't necessarily

  have to start with a course but say I

  want to be able to do X Y or Z and in

  order to do that I'm gonna need to learn

  a b and c where can I go to learn a b

  and c so that way I can do X Y or Z

  Etc if you approach it that way with a

  very specific project that is

  interesting to you you might be more

  likely to stick with it in the long haul

  rather than signing up for a course and

  going wow it's going to take like six

  weeks to get to something I'm actually

  interested in so instead I'm just gonna

  you know not learn this thing I also

  agree with the Hands-On activities like

  there are so many times that I watched a

  video and then I went and actually tried

  it in an IDE so a development

  environment where I was like oh wow I

  don't actually understand this thing I

  thought I understood this concept and

  then when I actually tried to do it like

  I completely failed and so then you've

  got to watch more videos or read more

  documentation to figure out why it

  didn't work because the instructor of

  that particular video or resource that

  you did go through didn't actually

  mention all the ways that it could go

  wrong and then be patient totally agree

  with that like it takes time don't beat

  yourself up if you aren't uh completely

  fluent in whatever programming languages

  within like the first week especially if

  you know you're working full time now

  chat GPT mentioned that okay if somebody

  who is new to computer science and wants

  to learn how to do computer science in

  the classroom you can do professional

  development workshops and courses you

  can do some online resources and

  tutorials like CS unplugged scratch and

  co.org were specifically mentioned you

  can engage in collaboration and

  networking I'd argue the csta community

  is a great way that you can do that

  there's discussion boards Etc there's

  also your local chapters check out the

  interview with Jason bour which is

  episode 82 and is titled Lessons Learned

  From csta chapters across the United

  States with Jason bour if you want to

  learn more about that I also mentioned

  Partnerships and mentoring this was

  interesting so this was like partnering

  with like a local business University or

  like industry professionals to have

  people come in and kind of like provide

  some guest instruction or lectures Etc

  or kind of like Mentor you through this

  process so that way you can learn on

  your own I do know some people who like

  doing stuff like that who are in the

  field and want to show you oh you want

  to learn about CS content knowledge I

  can help you with that you can't

  necessarily help you with how to teach

  it but I can help you with how to learn

  computer science and then you can figure

  out how to teach it they also mentioned

  there are many curriculum and resources

  that can align with this you can engage

  in peer learning and communities of

  practice but I kind of Wonder okay how

  is that different than collaboration and

  networking and then the last thing that

  chat GPD mentions is continuous learning

  and reflection I totally agree with that

  like it's never going to end if you're

  going into any kind of tech related or

  even steam or STEM related field it's

  likely going to be changing quite

  frequently then you're going to need to

  be able to continue to learn and improve

  as an educator and reflect on what's

  working well what can you improve Etc

  and you can do that at the end of like

  each lesson or day or unit project

  semester year Etc like having those

  different periods can kind of reflect on

  the micro like the daily or the lesson

  that happened as well as the macro like

  how did the unit go or how did the

  semester go and kind of making smaller

  and larger changes I think is generally

  beneficial for educators to engage in I

  highly recommend doing that all right

  another question that I ask the AI is

  what is computational thinking and why

  is it being taught now Bart's response

  was quite frankly not very good but at

  the beginning it says quote

  conversational thinking is a

  problem-solving approach that uses

  abstraction decomposition pattern

  recognition algorithms and evaluation it

  is a way of thinking that can be used to

  solve problems in any domain not just

  computer science end quote that is a

  very generic high level statement on

  what computational thinking is but the

  rest of the responses quite frankly

  didn't really do a great job explaining

  it so let's go to chat GPT chatgpt

  describes computational thinking as

  quote a problem-solving approach

  inspired by the principles used in

  computer science it involves breaking

  down complex problems into smaller more

  manageable components formulating

  algorithms or step-by-step procedures to

  solve them and utilizing logical and

  systematic thinking to arrive at

  Solutions computational thinking

  encompasses a set of skills and

  strategies that are transferable across

  various domains not just limited to

  computer science end quote then it goes

  on to say well here are some of like the

  main aspects of computational thinking

  there's decomposition pattern

  recognition abstraction algorithmic

  design algorithmic evaluation and that

  is being taught in schools because it

  helps with problem-solving skills

  because of digital literacy and

  technological understanding because it

  helps prepare for future careers because

  of critical and logical thinking because

  of creativity and Innovation integration

  with course subjects and future proofing

  students again with all of these like

  there's like a paragraph for each one of

  those things that I just quickly read

  off and you can read the whole thing in

  the show notes additional thinking is

  one of those terms that like so many

  people just get really uptight about

  like they have their one way of

  understanding this term and it's likely

  not in alignment with everybody else's

  understanding of that term because of

  that there's just constant budding of

  heads back and forth well what is it is

  it thinking is it a literacy is it a set

  of tools is it a process is it a

  framework blah blah blah blah and

  because there's really no one consensus

  on this I'm curious if your

  understanding of computational thinking

  aligned with either Bard's short

  definition or chat gpt's definition as

  well as the different

  aspects of computational thinking what

  was left out what was included that

  you're like yeah that totally lines or I

  disagree with that now I wanted to dive

  a little bit deeper with this idea of

  computational thinking so I asked AI to

  list five examples of computational

  thinking and in this scenario Bart

  actually did better with this so thank

  you Bart I appreciate you please spare

  me when you take over the world so

  here's an example decomposition quote

  breaking down a problem into smaller

  more manageable steps for example if you

  were trying to program a robot to walk

  you might break the problem down into

  steps like moving the legs moving the

  arms and balancing the body end quote

  now these examples are provided for

  pattern recognition abstraction

  algorithms and evaluation so if you want

  to see what the examples are for each

  one of those they're actually pretty

  decent chat GPT also provided some good

  examples of computational thinking so

  saying creating a recipe solving a

  Sudoku puzzle planning a route

  developing an app or conducting data

  analysis and so for each one of these

  they list like a sentence or two kind of

  explaining how each one of those

  examples is an example of computational

  thinking according to our future AI

  overlords the next question that I asked

  AI is write me a lesson that teaches an

  elementary student how to create a short

  scratch.mit.edu story this was

  fascinating folks as somebody who has

  developed over 100 lesson plans that's

  used by hundreds of thousands of

  students and thousands of teachers

  around the world this was something that

  I did professionally for the past like

  six or so years before I decided to

  leave that organization to be able to

  create content on my website and YouTube

  channel like this podcast as well as all

  the gaming and drumming stuff most of my

  lesson plans averaged maybe about 12

  Pages worth of content because there was

  so much information provided for that

  range of like experienced educator or

  somebody who is brand new to not only

  teaching but also brand new to computer

  science so there's a ton of resources

  and and what not to try and support them

  I would argue that these lesson plans

  are great for an intermediate to

  Advanced Computer Science educator who

  has taught computer science for a couple

  years and I was like yeah I got this I

  understand it just give me some of the

  the main points and I'll be able to

  figure this out the lesson plans that

  both Bart and chap GPT would not be

  helpful for somebody who's brand new to

  computer science they look at this and

  go okay well how do I do that or wait

  what what am I supposed to do here

  before a typical educator the fact that

  this was able to just like pump out a

  lesson plan that had objectives and

  materials introduction brainstorming

  storyboarding a coding session a testing

  session sharing it had assessments it

  had extensions conclusions Etc all of

  that to be popped out of an AI within a

  matter of moments was quite frankly

  impressive because I spent months just

  working on individual releases of like

  Junior literally months of full-time

  work just working on that and then AI is

  able to just like pump out an outline

  really fast like if I had said hey can

  you write this for somebody who's never

  taught computer science before or never

  taught a day in their life what would AI

  be able to do like 10 years from now

  would they be able to get the same level

  of quality and level of detail that I

  had with the 12 Pages or so on average

  of lesson plan content that I created

  for each one of the projects we'll find

  out I wouldn't be surprised if it can do

  that but that being said this stuff was

  very generic like if we look at the

  assessment for Bard it says that quote

  students will be assessed on their

  ability to create a short scratch story

  they will be evaluated on their

  creativity their coding skills and their

  ability to follow instructions end quote

  Yeah and how are you going to do that

  what about their creativity what

  specifically in their coding skills are

  you looking at and what instructions are

  they being asked to follow in their

  particular projects so there's like zero

  support in all of that that's just

  something that is just like oh yeah

  we're gonna do this thing and an

  administrator is going to be like cool

  how are you going to do that it's not

  going to go over well other examples of

  just like generic stuff that I would say

  is like not great advice was like in the

  introduction Bard mentioned quote

  introduce scratch to students and

  explain that it is a programming

  language that can be used to create

  interactive stories games and animations

  show students some examples of scratch

  project that have been created by other

  students end quote okay like what kinds

  of scratch projects like if I were

  writing that I'd say show students

  examples of stories that were created in

  scratch and here is a link to a studio

  that has like 10 different stories as

  examples that you can share with your

  students from simple to complex

  something like that would have been nice

  in this kind of lesson plan but again

  for just creating an outline really fast

  prior curriculum developer still I would

  probably go into this and just be like

  write me out an outline for something

  I'm not familiar with and then I can

  fill in the blanks with like oh a new

  teacher might need more support here or

  there so I do see a lot of like people

  using this in the future now chat CPT on

  the other hand had some interesting

  little improvements to it like for the

  introduction you're going to spend about

  you view you're going to spend about

  five minutes on this and you're going to

  quote show them how to access

  scratch.mit.edu and introduce the

  different components such as the stage

  Sprites and blocks in quote then it goes

  on to say specific blocks that you're

  even going to point out like the wind

  green flag has clicked the say and the

  weight block which all three of those

  are fantastic things that you would use

  in an actual story in scratch so like

  hands down chat GPT is like pretty solid

  for giving you a very like Bare Bones

  lesson plan on scratch but Bard was just

  more of like a generic lesson plan and

  it wasn't really clear if Byrd actually

  knew what scratch was other than a

  coding platform Chachi PT also expanded

  this and so it had that like the

  introduction okay here's what scratches

  then it had a brainstorming and planning

  phase which is for 10 minutes then it

  had creating the story for 20 minutes

  and it like showed you here's how to use

  the broadcast here's when the when the

  green flag is clicked like it it had

  some nuances in there I was like all

  right we're getting closer we're getting

  warmer but then it had adding an

  interactivity for like a 10 minute

  section so this is talking about like

  how to make a more complicated story

  with programming so not just like making

  it so that when you press a green flag

  it just like does does this thing and

  your story is over but actually making

  it so that you can have like different

  choices that affect the plot that was a

  really cool addition that chat gbt added

  that makes things much more creative and

  much more interactive for the user and

  for the student and whatnot who would

  create this so again you can see this

  like full lesson plans for both Bard and

  chap gbt in the show notes here's where

  things get really interesting I then had

  the exact same prompt but I added with

  the words about algorithmic bias so the

  prompt to both Bard and Chachi PT was

  write me a lesson that teaches an

  elementary student how to create a short

  scratch.mit.edu story about algorithmic

  bias here is what Bard said quote I'm

  unable to help as I am only a language

  model and don't have the ability to

  process and understand that end quote

  that's it nothing else is provided other

  than that single sentence jat gbt on the

  other hand provided basically a very

  similar lesson plan as was previously

  written without the words about rhythmic

  a bias but it included some comments in

  here like quote show examples of

  algorithmic bias in real life scenarios

  such as biased facial recognition

  systems or gender biased job application

  algorithms end quote that is actually

  like two really good examples that are

  often discussed in computer science so

  this was nice that chat gbt was actually

  able to include that whereas Bard was

  not able to do that or was unwilling to

  do that which is fascinating because I

  then took that exact same sentence and

  added in a little bit more after that

  original sentence and we get write me a

  lesson plan that teaches an elementary

  student how to create a short scratch

  that mit.edu story about algorithmic

  bias that's all the same while

  encouraging students to incorporate

  their own interests needs desires Etc in

  the story suddenly with that addition in

  there Bard was actually able to create a

  lesson plan until it again populates out

  the lesson plan specifically about all

  the algorithmic bias and ads and stuff

  like quote encourage them to think about

  their own interests needs and desires

  when coming up with ideas some possible

  story ideas include a story about a

  student who is denied a loan because of

  their race or gender a story about a job

  applicant who is not hired because of

  their age or disability a story about a

  person who is targeted by online

  advertising because of their political

  beliefs end quote so Bard was actually

  able to do it but not when it only

  focused on algorithmic bias but when I

  stead said hey focus on algorithmic bias

  but allow students to express themselves

  basically through the project so that

  was really weird I don't know why that

  was the response to it and then Chachi

  BT also was able to do this and so it

  included some like more forms of

  algorithmic bias while also encouraging

  students to be able to kind of Express

  themselves and explore their own

  interest in the project itself so

  overall the more specific you can be

  with the lesson plans the more at least

  in this very small set of lesson plans

  that I asked for the more you're

  actually going to be able to get from

  the different AI that you are

  potentially collaborating with when

  creating your own content now the very

  last question that I ask both the AI was

  based on the kinds of questions I've

  already asked today what questions

  should I ask you next and this is

  something that I think like can be done

  in general when engaging in these like

  discussions or collaborations with AIS

  to figure out okay based on the series

  of questions like what have I not asked

  that I probably should that I'm not

  necessarily thinking of now Bart kind of

  like failed right here so it's suggested

  questions like quote what is the

  difference between Ai and ml how does

  llms work what are some of the

  applications of NLP what are some of the

  challenges of ml what is the future of

  data science what are some of the best

  programming languages to learn end quote

  now llm is large language models like

  chat gbt and Bard NLP is natural

  language processing ml is machine

  learning okay so it does provide a

  little guide for you in in that response

  to that those questions quite frankly

  were not very interesting chat GPT on

  the other hand gave five different

  categories of questions that I could

  have followed up with so there's a

  category of strategies for teaching

  computer science so one of the three

  questions that I gave was what are some

  resources or tools that can support

  Hands-On learning in computer science

  under the ethical considerations in

  technology it had three questions and

  again here's one of them are there any

  emerging ethical issues related to

  artificial intelligence that students

  should be aware of that's a good

  question another category is

  professional development for teachers so

  again I gave three different questions

  and so one of them is how can teachers

  without a computer science background

  receive support in teaching computer

  science effectively the next category is

  integrating computer science across the

  curriculum and there's three questions

  for this one of them is how can computer

  science Concepts be integrated into

  subjects like math science or language

  arts and then the last category is on

  engaging underrepresented groups in

  computer science and so one of the three

  questions is how can we address the

  gender or diversity Gap in the tech

  industry from An Early Education

  perspective and again if you want to see

  the other questions that I left out on

  there in the shout outs jaredalary.com

  com for this particular episode I

  include every single one of the prompts

  and every single one of the responses

  from both Bard and chat gbt because it's

  over 50 pages worth of content it's just

  a click to expand and it'll show you

  exactly what the response was now what's

  fascinating is you can actually just

  like basically refresh the response to

  get a different answer in here so I do

  see this as a really interesting way for

  Content creators whether you're like a

  presenter or a someone who publishes or

  somebody who creates like videos like or

  a podcast or whatever you could totally

  use these different AI platforms to put

  in a a prompt or a question and just get

  some general ideas of topics that you

  can discuss at this point it's not

  necessarily at the point where it can

  get Beyond like the marketing buzzwordy

  responses for a lot of the prompts that

  you're going to give it but it can at

  least point in a direction of oh that

  one out of those five ideas is actually

  a really neat idea that I can elaborate

  on and expand upon in the presentation

  publication Etc I personally am going to

  start using chat GPT in particular

  because it was quite frankly better than

  Bard but both of them and kind of like

  just hitting the refresh button on

  different questions and prompts just so

  I can get some different perspectives

  because again large language models are

  basically synthesizing discourse across

  many different areas and kind of like

  repackaging it in a new way and

  basically saying here is like a summary

  of this idea it's not necessarily right

  or wrong depending on what kind of

  you're asking it but it's at least like

  predicting here's what somebody might

  say if they are sort of knowledgeable

  about this topic area and then as a

  content creator I can then go into this

  and go cool I want to take this and use

  this so if you're an educator and you

  are like have been teaching computer

  science for a couple years and maybe

  there's like a new thing that happens

  that's like a related to current events

  you could go into something like chat

  chippy Dean like hey write me this like

  quick lesson plan on this thing that is

  relevant to students right now and then

  that way you can get a very quick

  outline and you can fill in the blanks

  in there and go okay I'm gonna change

  this I'm gonna add this to here I'm

  gonna make this more relevant to this

  particular individual etc etc so I do

  see this as being a very helpful tool

  for educators for Content creators Etc

  and again content creators however you

  think of that whether you like present

  at csta which is going to happen here in

  July or you like publish on your own

  blog or create podcast or whatever I

  think this is helpful I also think it's

  helpful for students but there's going

  to be some coaching that needs to occur

  so that way students realize that AI is

  not here to solve the answer for you but

  is something that you can collaborate

  with and then you can then modify and

  add to and expand upon what the AI

  created rather than just copy and paste

  cool I'm done with my homework

  assignment don't need to write any more

  essays etc those are my own thoughts and

  Rants and whatnot on these particular

  questions if you have your own thoughts

  that you'd like to share you can share

  them in the comments section on YouTube

  this episode was very different from all

  the other unpacking scholarship episodes

  and interviews that I've done but I hope

  you enjoyed this this conversation was

  informed by discussions with GT Rubble

  Sarah Judd and John Stapleton thank you

  for the three of you all of whom have

  been interviewed on this podcast so you

  can listen to their interviews by just

  clicking the links in the show notes for

  this particular episode stay tuned next

  week for a podcast episode that's also

  going to be a little bit different where

  I'm actually going to talk about some

  suggestions for how you can get started

  in computer science without actually

  having to pay for paid professional

  development so stay tuned for that if

  you enjoyed this episode please consider

  sharing with somebody else or leaving a

  like on YouTube or a review on whatever

  app that you're listening to the song

  like an apple podcast or something like

  that it just helps more people find it

  but again the best thing that you could

  possibly do is to share it with somebody

  else there are hundreds if not thousands

  of more free computer science education

  resources on my website jaredoliri.com

  as well as over 1500 hours of free

  drumming content and a bunch of gaming

  content as well but it's all neatly

  organized so if you're interested in

  just one of those areas you don't have

  to see any of the other stuff thank you

  so much for listening to this episode

  and potentially sharing it with somebody

  else stay tuned for an episode next week

  until then I hope you're all staying

  safe and are having a wonderful week

Prompts and responses (click to expand)

1. What will teaching and learning look like ten years from now?

• Bard

  Teaching and learning will look very different in 10 years. Here are some of the changes that we can expect:

  • Personalized learning. Students will have more control over their own learning paths, and they will be able to learn at their own pace and in their own way. This will be facilitated by the use of educational technologies such as adaptive learning software and online learning platforms.

  • More emphasis on critical thinking and problem solving. In today's world, it is more important than ever for students to be able to think critically and solve problems. Schools will place more emphasis on these skills, and they will use a variety of methods to teach them, such as project-based learning and problem-based learning.

  • More collaboration and teamwork. In the future, the workplace will be increasingly collaborative, and schools will prepare students for this by emphasizing collaboration and teamwork in the classroom. Students will be given opportunities to work together on projects and assignments, and they will learn how to communicate effectively and resolve conflict.

  • More use of technology. Technology will play an increasingly important role in education in the future. Teachers will use technology to deliver instruction, and students will use it to learn and collaborate. Technology will also be used to assess student learning and to provide feedback.

  • More focus on global citizenship. In the future, students will need to be prepared to live and work in a globalized world. Schools will place more emphasis on global citizenship by teaching students about different cultures and perspectives. Students will also be given opportunities to learn about global issues and to take action to make a difference in the world.

  These are just some of the changes that we can expect to see in teaching and learning in 10 years. The future of education is bright, and it is exciting to think about the possibilities.

• ChatGPT

  Predicting the future with certainty is challenging, but we can speculate on potential trends and advancements in teaching and learning based on current developments. Here are some possibilities for what teaching and learning might look like ten years from now:

  1. Personalized Learning: With advancements in technology and the increasing availability of data, education could become more tailored to individual students' needs. Adaptive learning systems, AI tutors, and intelligent educational software could be utilized to create personalized learning paths for students, catering to their strengths, weaknesses, and interests.

  2. Blended Learning Environments: The integration of technology into education will likely continue to grow. Blended learning models, combining in-person instruction with online resources, could become the norm. Virtual classrooms, video conferencing, and online collaboration tools would enable remote learning and foster global connections among students and educators.

  3. Emphasis on Critical Thinking and Problem Solving: As the world becomes more complex and dynamic, the focus of education may shift towards developing students' critical thinking, problem-solving, and creative skills. Rote memorization might be de-emphasized in favor of cultivating students' abilities to analyze, synthesize, and evaluate information.

  4. Lifelong Learning and Upskilling: The rapid pace of technological advancements and evolving job markets may require individuals to continually update their skills and knowledge throughout their lives. Education systems might incorporate lifelong learning approaches, offering flexible programs and resources for upskilling and reskilling workers to adapt to changing demands.

  5. Collaboration and Project-Based Learning: Collaborative learning experiences and project-based approaches could gain prominence. Students may engage in hands-on, interdisciplinary projects that mirror real-world challenges, promoting teamwork, communication, and practical application of knowledge.

  6. Gamification and Immersive Experiences: Gamification elements, such as rewards, achievements, and interactive simulations, could be integrated into educational activities to enhance engagement and motivation. Virtual reality (VR) and augmented reality (AR) technologies might provide immersive learning experiences, allowing students to explore and interact with subjects in a more interactive and memorable way.

  7. Data-Driven Education: The collection and analysis of educational data could become more prevalent. Educators may leverage learning analytics and assessment data to gain insights into student progress, identify areas of improvement, and make data-informed decisions to enhance teaching effectiveness and student outcomes.

  8. Cultural and Global Awareness: Education could place a greater emphasis on fostering cultural understanding, empathy, and global citizenship. Students may have more opportunities to engage with diverse perspectives, learn about different cultures, and develop a sense of social responsibility and global interconnectedness.

  It's important to note that these predictions are speculative, and the future of teaching and learning will depend on various factors, including technological advancements, societal changes, and educational policies. Flexibility, adaptability, and a focus on nurturing essential skills will likely be key in preparing students for the challenges and opportunities of the future.

- How is that different from today?

• Bard

  Here are some of the key differences between teaching and learning today and what we can expect in 10 years:

  • Personalized learning: Today, students typically learn in a one-size-fits-all classroom setting. In 10 years, students will have more control over their own learning paths, and they will be able to learn at their own pace and in their own way. This will be facilitated by the use of educational technologies such as adaptive learning software and online learning platforms.

  • More emphasis on critical thinking and problem solving: Today, many schools focus on teaching students to memorize facts. In 10 years, schools will place more emphasis on teaching students to think critically and solve problems. This will be important in a world where information is constantly changing and where students will need to be able to think for themselves and come up with creative solutions.

  • More collaboration and teamwork: Today, students typically work on individual projects. In 10 years, students will be given more opportunities to work together on projects and assignments. This will help them to develop their collaboration and teamwork skills, which will be important in the future workplace.

  • More use of technology: Today, technology is often used in the classroom as a supplement to traditional instruction. In 10 years, technology will play a more central role in education. Teachers will use technology to deliver instruction, and students will use it to learn and collaborate. Technology will also be used to assess student learning and to provide feedback.

  • More focus on global citizenship: Today, many schools focus on teaching students about their own culture. In 10 years, schools will place more emphasis on teaching students about different cultures and perspectives. This will help students to become more global citizens and to understand the world around them.

  Overall, teaching and learning in 10 years will be more personalized, focused on critical thinking and problem solving, collaborative, technology-rich, and globalized. These changes will help students to develop the skills they need to succeed in the future.

• ChatGPT

  While some of the trends mentioned in the previous response are already present in today's educational landscape, there are several key differences that may become more pronounced or evolve over the next ten years. Here's a comparison between the projected future and the current state of teaching and learning:

  1. Personalized Learning: Personalized learning is already gaining traction today, but it is often limited to specific schools or programs. In the future, advancements in technology and data analysis may allow for more widespread implementation of personalized learning approaches, tailoring education to the individual needs of each student.

  2. Blended Learning Environments: Blended learning, combining in-person and online components, is already being used in many educational settings. However, the COVID-19 pandemic has accelerated its adoption and highlighted the need for remote learning options. In the future, we may see a more seamless integration of virtual classrooms, online resources, and interactive tools to create more effective blended learning experiences.

  3. Emphasis on Critical Thinking and Problem Solving: While critical thinking and problem-solving skills have always been important, there is a growing recognition of their significance in today's rapidly changing world. Efforts to promote these skills are being made, but the future may see a stronger shift away from traditional rote memorization methods towards more active learning approaches that foster critical thinking and problem-solving abilities.

  4. Lifelong Learning and Upskilling: Lifelong learning has been increasingly emphasized in recent years, and the need for upskilling and reskilling is becoming more evident. However, the pace of technological advancement suggests that lifelong learning will become even more crucial in the future, with education systems adapting to provide flexible and accessible opportunities for individuals to continually update their skills.

  5. Collaboration and Project-Based Learning: Collaborative learning and project-based approaches are already being implemented in many classrooms. However, the future may see a greater emphasis on interdisciplinary projects, real-world applications, and global collaborations, leveraging technology to connect students and educators across geographical boundaries.

  6. Gamification and Immersive Experiences: Gamification and immersive technologies are still in the early stages of adoption in education. As these technologies become more accessible and affordable, we may see greater integration of game elements and immersive experiences to enhance engagement, motivation, and the overall learning process.

  7. Data-Driven Education: Educational data analytics and learning analytics are already being utilized to some extent, but their full potential is yet to be realized. In the future, advancements in data collection, analysis techniques, and privacy regulations may enable more comprehensive and actionable insights, allowing educators to make data-driven decisions and tailor instruction to individual student needs.

  8. Cultural and Global Awareness: Global awareness and cultural understanding are already valued in many educational curricula. However, with an increasingly interconnected world, the future may see a stronger focus on nurturing these skills, fostering empathy, and promoting cross-cultural communication to prepare students for a globalized society.

  It's worth noting that the pace of change and adoption varies across educational systems and regions, so the extent to which these differences manifest will depend on various factors such as infrastructure, resources, and educational policies.

2. What is the role of artificial intelligence in teaching and learning?

• Bard

  Artificial intelligence (AI) is rapidly changing the world, and education is no exception. AI has the potential to revolutionize teaching and learning by making it more personalized, engaging, and effective.

  Here are some of the ways that AI is being used in education today:

  • Personalized learning: AI can be used to create personalized learning experiences for students. This means that students can learn at their own pace and in their own way, based on their individual needs and interests.

  • Adaptive learning: AI can be used to create adaptive learning materials that adjust to the student's level of understanding. This means that students can get the help they need when they need it, without having to wait for the teacher.

  • Virtual tutors: AI can be used to create virtual tutors that can answer students' questions and provide feedback. This can be especially helpful for students who need extra help or who are learning remotely.

  • Assessment: AI can be used to assess student learning in a more accurate and efficient way. This can help teachers to identify students who are struggling and to provide them with the help they need.

  • Research: AI can be used to conduct research on education and to develop new teaching methods. This can help to improve the quality of education for all students.

• ChatGPT

  Artificial intelligence (AI) can play a significant role in transforming teaching and learning in several ways:

  1. Personalized Learning: AI algorithms can analyze vast amounts of data to create personalized learning experiences for students. By adapting content, pacing, and difficulty levels to individual students' needs, AI-powered educational platforms can provide tailored instruction, ensuring that students receive the right level of challenge and support.

  2. Intelligent Tutoring: AI tutors can provide individualized guidance and support to students. These virtual assistants can answer questions, provide explanations, and offer feedback, mimicking the role of a human tutor. AI tutors can be available 24/7, providing on-demand assistance to students whenever they need it.

  3. Automated Grading and Feedback: AI can automate the grading process for certain types of assessments, such as multiple-choice questions or coding exercises. This saves teachers time and allows for faster feedback to students. AI-powered systems can also provide detailed feedback, highlighting areas for improvement and offering targeted suggestions to help students progress.

  4. Natural Language Processing (NLP): NLP enables computers to understand and generate human language. AI-powered chatbots or virtual assistants can engage in conversations with students, answering questions, providing explanations, and engaging in interactive dialogue. NLP can also be used to develop language learning applications, language translation tools, and speech recognition systems.

  5. Data Analysis and Learning Analytics: AI can analyze large datasets generated by students' interactions with educational platforms, helping identify patterns, trends, and insights. This data analysis can inform instructional design, provide feedback on student progress, and assist in identifying areas where additional support or intervention may be needed.

  6. Content Recommendation and Curation: AI algorithms can recommend relevant learning resources, books, articles, or videos based on students' interests, learning styles, and previous interactions. This helps students discover new materials and fosters self-directed learning.

  7. Intelligent Content Creation: AI can assist in generating educational content, such as automated lesson plans, quizzes, or interactive simulations. It can also provide assistance in creating adaptive learning materials that adjust to individual student needs.

  8. Virtual Reality and Augmented Reality: AI-powered virtual reality (VR) and augmented reality (AR) technologies can provide immersive and interactive learning experiences. These technologies can simulate real-world scenarios, allowing students to explore and interact with subjects in a more engaging and practical manner.

  It's important to note that while AI has the potential to enhance teaching and learning, it is not intended to replace human educators. Rather, AI can complement and support teachers by automating routine tasks, providing insights, and offering personalized assistance, thereby enabling educators to focus more on individualized instruction, mentoring, and fostering human connection in the classroom.

3. Why are teachers leaving the field and what can be done to prevent that?

• Bard

  There are many reasons why teachers are leaving the field. Some of the most common reasons include:

  • Low pay: Teachers are often underpaid for the amount of work they do. According to the National Education Association, the average teacher salary in the United States is $61,730. This is significantly lower than the average salary for other professionals with a college degree.

  • Heavy workload: Teachers often have a heavy workload, which can lead to stress and burnout. They are responsible for planning lessons, grading papers, and managing a classroom of students. They may also be required to work long hours outside of school, such as attending meetings or planning for field trips.

  • Lack of support: Teachers often feel unsupported by their schools and administrators. They may not have the resources they need to do their jobs effectively, such as small class sizes or access to technology. They may also feel like they are not being heard or that their concerns are not being addressed.

  • Unruly students: Teachers may be dealing with unruly students who are disruptive and disrespectful. This can be a major source of stress and can make it difficult to teach effectively.

  • Parental involvement: Some teachers feel like they do not have enough support from parents. Parents may not be involved in their children's education or may not be supportive of the teacher's methods.

  There are a number of things that can be done to prevent teachers from leaving the field. These include:

  • Increase teacher pay: Increasing teacher pay would help to attract and retain qualified teachers.

  • Reduce teacher workload: Reducing teacher workload would help to reduce stress and burnout. This could be done by reducing class sizes, providing more planning time, or offering more professional development opportunities.

  • Provide more support: Schools and administrators can provide more support to teachers by providing them with the resources they need, such as small class sizes or access to technology. They can also listen to teachers' concerns and take steps to address them.

  • Develop positive relationships with students: Teachers can develop positive relationships with students by being fair and consistent, showing respect, and being approachable.

  • Encourage parental involvement: Schools can encourage parental involvement by sending home regular newsletters, holding parent-teacher conferences, and inviting parents to volunteer at school.

  By addressing these issues, we can help to create a more positive and supportive environment for teachers. This will help to attract and retain qualified teachers, which is essential for ensuring that all students have access to a quality education.

• ChatGPT

  Teachers leave the field for various reasons, and it's a complex issue influenced by a combination of factors. Some common reasons why teachers choose to leave include:

  1. Low Salaries: In many regions, teacher salaries may not be competitive compared to other professions with similar levels of education and experience. Financial concerns can lead teachers to seek better-paying opportunities outside of education.

  2. Heavy Workload and Stress: Teaching can be demanding, with long working hours, administrative tasks, lesson planning, grading, and managing student behavior. The workload, combined with high expectations and accountability measures, can contribute to burnout and stress.

  3. Lack of Support and Professional Development: Teachers thrive when they receive ongoing support and opportunities for professional growth. A lack of resources, limited access to professional development, and inadequate support from administrators and colleagues can impact job satisfaction and lead teachers to seek more supportive environments.

  4. Classroom Management Challenges: Maintaining a positive learning environment can be challenging, especially when dealing with diverse student needs, disruptive behavior, or lack of resources. Teachers who feel overwhelmed by classroom management issues may consider leaving the field.

  5. Policy Changes and Standardized Testing: Frequent changes in education policies, including the emphasis on high-stakes standardized testing, can create pressure on teachers to focus on test preparation rather than holistic teaching and student development. Such shifts may erode job satisfaction and diminish teachers' autonomy and creativity.

  To address the issue of teachers leaving the field, several strategies can be implemented:

  1. Competitive Compensation: Providing teachers with competitive salaries and benefits can attract and retain talented individuals in the profession. Higher salaries can also serve as a recognition of the value and importance of the teaching profession.

  2. Supportive Work Environment: Creating a positive and supportive work environment is crucial. This includes fostering a collaborative culture, providing mentorship programs, and ensuring access to resources and professional development opportunities.

  3. Reduced Administrative Burden: Reducing unnecessary administrative tasks and paperwork can free up teachers' time and allow them to focus more on instruction and student engagement.

  4. Improved Classroom Management Strategies: Providing training and support for effective classroom management strategies can help teachers better handle challenging student behavior and create a positive learning environment.

  5. Professional Development and Growth: Investing in high-quality professional development programs that cater to teachers' needs and interests can enhance their skills, boost job satisfaction, and promote retention. Professional development should focus on innovative instructional practices, technology integration, and strategies for meeting the diverse needs of students.

  6. Teacher Voice and Collaboration: Involving teachers in decision-making processes, policy development, and curriculum design can empower them and enhance their sense of ownership and professional autonomy.

  7. Teacher Well-being and Mental Health Support: Prioritizing teacher well-being and providing access to mental health support can help address burnout and stress-related issues. Implementing wellness programs, offering counseling services, and promoting work-life balance can contribute to teacher retention.

  Addressing the issue of teacher attrition requires a comprehensive approach that involves collaboration among policymakers, administrators, educators, and stakeholders in the education system. By prioritizing the needs and well-being of teachers, providing ongoing support, and creating conducive working environments, it is possible to mitigate factors leading to teacher departures and improve teacher retention rates.

4. How much should a public elementary school computer science educator be paid in Arizona with ten years of experience and a doctorate in education?

• Bard

  According to the Bureau of Labor Statistics, the median annual salary for elementary school teachers in Arizona was $56,590 in 2020. However, salaries can vary depending on experience, education, and location. A public elementary school computer science educator with ten years of experience and a doctorate in education could expect to earn a salary of $75,000 or more.

  Here are some factors that can affect the salary of a public elementary school computer science educator in Arizona:

  • Experience: Teachers with more experience typically earn higher salaries.

  • Education: Teachers with higher levels of education, such as a doctorate in education, typically earn higher salaries.

  • Location: Teachers in urban areas typically earn higher salaries than teachers in rural areas.

  • School district: The salary of a teacher can also vary depending on the school district they work for.

  It is important to note that these are just estimates and the actual salary of a public elementary school computer science educator in Arizona can vary depending on the specific factors mentioned above.

  Here are some of the benefits of being a public elementary school computer science educator in Arizona:

  • Job security: There is a high demand for qualified computer science educators in Arizona.

  • Good benefits: Public school teachers in Arizona typically receive good benefits, such as health insurance, retirement plans, and paid time off.

  • Impactful work: Teachers have the opportunity to make a real difference in the lives of their students.

  If you are interested in becoming a public elementary school computer science educator in Arizona, there are a few things you can do to increase your chances of getting a job. First, make sure you have a strong academic background in computer science. Second, get experience teaching computer science, either through volunteer work or a part-time job. Finally, network with other computer science educators in Arizona.

• ChatGPT

  As an AI language model, I don't have access to real-time salary data or specific information about salary scales in Arizona. Teacher salaries can vary based on factors such as location, school district, budget, and negotiation between teachers' unions and education authorities. Therefore, it's best to consult reliable sources for up-to-date and accurate salary information.

  To determine the specific salary range for a public elementary school computer science educator in Arizona with ten years of experience and a doctorate in education, you can refer to the following sources:

  1. Arizona Department of Education: The Arizona Department of Education may provide information on salary scales for teachers in the state. They may have salary schedules or guidelines available on their official website.

  2. School District Websites: Each school district in Arizona may have its own salary schedule, which can be found on their respective websites. By visiting the websites of the school districts where you are interested in working, you may find salary information specific to that district.

  3. Teachers' Union: The local teachers' union or association in Arizona may have information on salary scales, collective bargaining agreements, and other relevant details for educators in the state.

  By accessing these sources or reaching out to the appropriate education authorities in Arizona, you can gather accurate and up-to-date information about the salary range for a public elementary school computer science educator with ten years of experience and a doctorate in education.

- How much should a private elementary school educator be paid in Arizona with ten years of experience and a doctorate in education?

• Bard

  The salary of a private elementary school educator in Arizona with ten years of experience and a doctorate in education can vary depending on the school, but it is typically higher than the salary of a public school educator with the same qualifications. According to a 2021 survey by the National Association of Independent Schools, the median salary for elementary school teachers in private schools was $72,000. However, salaries can range from $50,000 to $100,000 or more, depending on the school's location, size, and budget.

  There are a few reasons why private school teachers are typically paid more than public school teachers. First, private schools are often able to charge higher tuition, which allows them to pay their teachers more. Second, private schools often have smaller class sizes, which allows teachers to spend more time with each student. Third, private schools often have more resources, such as technology and materials, which can help teachers to be more effective.

  If you are interested in becoming a private elementary school educator in Arizona, there are a few things you can do to increase your chances of getting a job. First, make sure you have a strong academic background in education. Second, get experience teaching elementary school, either through volunteer work or a part-time job. Finally, network with other private school educators in Arizona.

  Here are some of the benefits of being a private elementary school educator in Arizona:

  • Higher salaries: Private school teachers in Arizona typically earn higher salaries than public school teachers.

  • Smaller class sizes: Private schools in Arizona typically have smaller class sizes than public schools.

  • More resources: Private schools in Arizona typically have more resources, such as technology and materials, than public schools.

  • More autonomy: Private school teachers in Arizona typically have more autonomy than public school teachers.

  If you are looking for a rewarding career in education, becoming a private elementary school educator in Arizona is a great option. With a strong academic background, experience teaching elementary school, and a network of contacts, you can increase your chances of finding a job at a private school that is a good fit for you.

• ChatGPT

  Determining the specific salary for a private elementary school educator in Arizona with ten years of experience and a doctorate in education can be challenging as private schools often have more variability in their salary structures compared to public schools. Private school salaries are typically determined by factors such as the school's budget, location, size, reputation, and the demand for educators with specific qualifications.

  To get an accurate and up-to-date understanding of the salary range for a private elementary school educator in Arizona with your specified qualifications, you can consider the following approaches:

  1. Research Private School Websites: Visit the websites of private schools in Arizona, especially those that are similar in size, reputation, and location to the school you are interested in. Some private schools may provide salary information on their websites or have contact information available for inquiries about employment and compensation.

  2. Networking and Professional Organizations: Connect with educators working in private schools in Arizona, join professional organizations or forums related to private school education, and engage in discussions about salary ranges and compensation packages. Networking with individuals in the field can provide you with insights into the typical compensation for private school educators in the state.

  3. Consult Salary Surveys and Reports: Look for salary surveys and reports specific to private school educators in Arizona or similar regions. These surveys may provide average salary ranges or data on compensation trends within the private school sector.

  4. Contact Private Schools Directly: Reach out to private schools in Arizona that you are interested in working for and inquire about their salary ranges and benefits for educators with your qualifications. It may be beneficial to communicate directly with the school's administration or human resources department to gather specific information.

  Remember that salary ranges can vary significantly between private schools, and negotiations for compensation packages may be part of the hiring process. Conducting thorough research, networking with professionals in the field, and directly contacting schools will help you obtain the most accurate and relevant salary information for private elementary school educators in Arizona.

5. What are the top five pedagogies for teaching computer science?

• Bard

  Here are the top five pedagogies for teaching computer science:

  • Project-based learning: Project-based learning (PBL) is a student-centered approach to learning that involves students working on a real-world problem or challenge. PBL can be a great way to teach computer science because it allows students to apply what they are learning in a meaningful way.

  • Problem-based learning: Problem-based learning (PBL) is another student-centered approach to learning that involves students working on a complex problem. PBL can be a great way to teach computer science because it allows students to develop their problem-solving skills.

  • Approaches to Computational Thinking: Approaches to Computational Thinking (ACT) is a framework for teaching computer science that focuses on the process of thinking like a computer scientist. ACT can be a great way to teach computer science because it helps students to develop their critical thinking and problem-solving skills.

  • Inductive Inquiry: Inductive Inquiry is a teaching method that emphasizes discovery learning. Inductive Inquiry can be a great way to teach computer science because it allows students to explore and learn at their own pace.

  • Experiential Learning: Experiential Learning is a teaching method that emphasizes hands-on learning. Experiential Learning can be a great way to teach computer science because it allows students to learn by doing.

  These are just a few of the many pedagogies that can be used to teach computer science. The best pedagogy for a particular classroom will depend on the students, the teacher, and the resources available.

  Here are some additional tips for teaching computer science:

  • Make it relevant: Computer science can be a challenging subject for some students, so it is important to make it relevant to their lives. This can be done by using real-world examples and applications.

  • Use visuals: Visuals can help students to understand complex concepts in computer science. This can be done by using diagrams, flowcharts, and animations.

  • Make it fun: Computer science should be fun! This can be done by using games, puzzles, and other activities.

  • Provide feedback: Students need feedback in order to improve their skills in computer science. This feedback can be provided by the teacher, peers, or self-assessment.

  • Celebrate successes: It is important to celebrate students' successes in computer science. This will help them to stay motivated and engaged.

  By following these tips, you can help your students to learn computer science in a fun and engaging way.

• ChatGPT

  When it comes to teaching computer science, several pedagogical approaches can be effective in engaging students and fostering their understanding of the subject matter. Here are five pedagogies commonly used in computer science education:

  1. Project-Based Learning (PBL): Project-Based Learning involves students working on real-world projects or challenges that require them to apply their computer science knowledge and skills. Through hands-on, collaborative projects, students gain practical experience, develop problem-solving skills, and learn to work in teams. PBL encourages critical thinking, creativity, and the integration of various computer science concepts.

  2. Inquiry-Based Learning: Inquiry-Based Learning focuses on student-driven exploration and discovery. In computer science, this pedagogy encourages students to ask questions, investigate problems, and find solutions independently. By promoting curiosity and active engagement, inquiry-based learning helps students develop problem-solving abilities, research skills, and a deeper understanding of fundamental computer science concepts.

  3. Collaborative Learning: Collaborative Learning emphasizes group work and collaboration among students. It encourages peer-to-peer interaction, discussion, and cooperative problem-solving. In computer science, collaborative learning can involve pair programming, group projects, and team-based coding challenges. This pedagogy enhances communication skills, teamwork, and the ability to work effectively in diverse teams.

  4. Flipped Classroom: The Flipped Classroom approach involves delivering instructional content outside of class (e.g., through videos, online modules, or readings) and utilizing class time for hands-on activities and discussions. In computer science, students can watch video tutorials or complete online exercises before coming to class, enabling them to engage in practical coding, problem-solving, and concept reinforcement during class sessions. Flipped classrooms promote active learning, student engagement, and individualized support.

  5. Game-Based Learning: Game-Based Learning incorporates educational games, gamified elements, or game design principles to teach computer science concepts. Students learn through interactive gameplay, simulations, and problem-solving within a game environment. Game-based learning promotes motivation, engagement, and a deeper understanding of programming concepts and algorithms.

  It's worth noting that these pedagogical approaches can be combined or adapted based on the specific needs of students, the learning objectives, and the available resources. Effective computer science instruction often involves a blend of these pedagogies, along with the integration of technology tools, formative assessment strategies, and differentiation to meet the diverse needs of learners.

- Elaborate on how each of the pedagogies you listed are beneficial for learning

• Bard

  Sure. Here is a more detailed look at how each of the pedagogies I listed are beneficial for learning:

  • Project-based learning: Project-based learning (PBL) is a student-centered approach to learning that involves students working on a real-world problem or challenge. PBL can be a great way to teach computer science because it allows students to apply what they are learning in a meaningful way. Students are more likely to remember and understand information when they are able to apply it to a real-world problem. PBL also helps students to develop their problem-solving, collaboration, and communication skills.

  • Problem-based learning: Problem-based learning (PBL) is another student-centered approach to learning that involves students working on a complex problem. PBL can be a great way to teach computer science because it allows students to develop their problem-solving skills. Students are faced with a problem and must work together to find a solution. This helps them to develop their critical thinking, creativity, and collaboration skills.

  • Approaches to Computational Thinking: Approaches to Computational Thinking (ACT) is a framework for teaching computer science that focuses on the process of thinking like a computer scientist. ACT can be a great way to teach computer science because it helps students to develop their critical thinking and problem-solving skills. ACT teaches students how to break down problems into smaller steps, identify patterns, and use algorithms to solve problems.

  • Inductive Inquiry: Inductive Inquiry is a teaching method that emphasizes discovery learning. Inductive Inquiry can be a great way to teach computer science because it allows students to explore and learn at their own pace. Students are given a problem or challenge and are allowed to explore different solutions until they find one that works. This helps them to develop their creativity and problem-solving skills.

  • Experiential Learning: Experiential Learning is a teaching method that emphasizes hands-on learning. Experiential Learning can be a great way to teach computer science because it allows students to learn by doing. Students are given the opportunity to build their own projects or programs. This helps them to develop their coding skills and problem-solving skills.

  These are just a few of the many pedagogies that can be used to teach computer science. The best pedagogy for a particular classroom will depend on the students, the teacher, and the resources available.

  I hope this helps!

• ChatGPT

  Certainly! Here's a more detailed explanation of the benefits of each pedagogy for learning computer science:

  1. Project-Based Learning (PBL):

     • Practical Application: PBL allows students to apply computer science knowledge in real-world contexts, making the learning experience more relevant and meaningful.

     • Problem-Solving Skills: By working on projects, students develop critical thinking, analytical skills, and problem-solving abilities, which are essential in computer science.

     • Collaboration: PBL promotes teamwork and collaboration as students work together to tackle complex problems, improving communication, and fostering social skills.

     • Creativity and Innovation: PBL encourages students to think creatively and come up with innovative solutions, fostering their ability to think outside the box.

  2. Inquiry-Based Learning:

     • Critical Thinking: Inquiry-based learning promotes critical thinking as students explore questions, analyze data, and draw conclusions, developing their ability to think critically and evaluate information.

     • Autonomy and Ownership: By directing their own learning, students become active participants in their education, taking ownership of their learning process, and developing self-directed learning skills.

     • Problem-Solving and Research Skills: Through inquiry-based learning, students develop research skills, learn how to frame and investigate questions, and develop problem-solving strategies.

     • Deeper Understanding: By engaging in inquiry, students develop a deeper understanding of computer science concepts as they actively seek answers and make connections.

  3. Collaborative Learning:

     • Communication and Collaboration: Collaborative learning encourages students to work together, enhancing their communication, collaboration, and teamwork skills, which are essential in the field of computer science.

     • Peer Learning: Students can learn from their peers by discussing concepts, explaining ideas, and providing feedback to one another, which can reinforce learning and deepen understanding.

     • Diverse Perspectives: Collaboration exposes students to diverse perspectives and approaches, fostering creativity and promoting critical thinking as they consider different solutions to problems.

     • Enhanced Problem-Solving: Collaborative problem-solving allows students to tackle complex tasks collectively, drawing on the strengths and expertise of each team member, leading to more comprehensive solutions.

  4. Flipped Classroom:

     • Active Learning: Flipped classrooms promote active learning by shifting passive content consumption outside of class, allowing students to engage in hands-on activities, coding exercises, and discussions during class time.

     • Differentiated Instruction: Students can learn at their own pace and review content as needed outside of class, while in-class activities provide opportunities for individualized support, addressing specific student needs.

     • Immediate Feedback: In-class activities facilitate immediate feedback from the instructor, allowing students to receive guidance, clarification, and corrections in real-time.

     • Application and Reinforcement: Flipped classrooms provide opportunities for students to apply and reinforce their understanding of computer science concepts through practical coding exercises and problem-solving tasks.

  5. Game-Based Learning:

     • Motivation and Engagement: Games capture students' interest, motivate them to actively participate, and create an engaging learning experience, promoting sustained focus and enjoyment in computer science education.

     • Hands-on Learning: Games provide hands-on experiences, allowing students to experiment, make decisions, and observe immediate consequences, leading to a deeper understanding of programming concepts.

     • Immediate Feedback: Games can offer immediate feedback, guiding students through challenges and providing corrective guidance, fostering a sense of mastery and progress.

     • Trial and Error: Games encourage students to take risks, try different strategies, and learn from their mistakes, promoting resilience, problem-solving skills, and a growth mindset.

  These pedagogical approaches are not mutually exclusive and can be combined to create dynamic and engaging learning experiences in computer science education. By leveraging these approaches, educators can enhance student motivation, critical thinking, problem-solving skills, collaboration, and a deeper understanding of computer science concepts.

- What are the downsides for each of the pedagogies you listed?

• Bard

  Here are some of the downsides for each of the pedagogies I listed:

  • Project-based learning: Project-based learning (PBL) can be time-consuming and requires a lot of planning and preparation from the teacher. It can also be difficult to assess student learning in a project-based setting.

  • Problem-based learning: Problem-based learning (PBL) can be challenging for students who are not used to working independently or collaborating with others. It can also be difficult for students to find problems that are both challenging and relevant to their interests.

  • Approaches to Computational Thinking: Approaches to Computational Thinking (ACT) can be difficult for students who are not already familiar with the basics of computer science. It can also be difficult for teachers to find resources that support ACT instruction.

  • Inductive Inquiry: Inductive Inquiry can be chaotic and difficult to manage in a large classroom setting. It can also be difficult for students to stay on track and make progress towards their goals.

  • Experiential Learning: Experiential Learning can be expensive, as it often requires students to purchase materials or equipment. It can also be difficult for teachers to find time to provide individual feedback to students.

  It is important to note that these are just some of the potential downsides of each pedagogy. The actual downsides may vary depending on the specific classroom context.

  Here are some tips for overcoming the downsides of each pedagogy:

  • Project-based learning: To reduce the time commitment required for PBL, teachers can choose projects that are shorter or that focus on a single concept. To make assessment easier, teachers can develop rubrics that clearly define the criteria for success.

  • Problem-based learning: To make PBL more accessible to all students, teachers can provide support and scaffolding throughout the process. To help students find problems that are relevant to their interests, teachers can encourage students to choose problems from their own lives or from current events.

  • Approaches to Computational Thinking: To make ACT instruction more accessible to students, teachers can provide hands-on activities and games that introduce the concepts of computational thinking. To help teachers find resources that support ACT instruction, there are a number of online resources available, such as the Khan Academy and Code.org.

  • Inductive Inquiry: To help manage Inductive Inquiry in a large classroom setting, teachers can break students into small groups or pairs. To help students stay on track, teachers can provide regular checkpoints and feedback.

  • Experiential Learning: To reduce the cost of Experiential Learning, teachers can encourage students to use recycled materials or to find free or low-cost resources online. To make it easier for teachers to provide individual feedback, teachers can use peer assessment or group work.

  By following these tips, teachers can overcome the downsides of each pedagogy and create a learning environment that is both engaging and effective for all students.

• ChatGPT

  While the pedagogies I listed have many benefits, it's important to consider potential downsides and challenges associated with their implementation. Here are some potential downsides for each pedagogy:

  1. Project-Based Learning (PBL):

     • Time Management: PBL projects can be time-consuming, requiring careful planning and coordination to ensure that the project's scope aligns with available instructional time.

     • Assessment Challenges: Assessing individual student learning in PBL can be complex since projects often involve group work. It can be challenging to accurately evaluate individual contributions and learning outcomes.

     • Content Coverage: PBL projects may focus on specific topics, potentially leaving gaps in other areas of the curriculum if not properly integrated and aligned with learning objectives.

  2. Inquiry-Based Learning:

     • Teacher Expertise: Implementing inquiry-based learning requires teachers who are skilled in guiding student inquiry, facilitating discussions, and supporting students in their exploration. Lack of teacher expertise in inquiry methods may hinder its effective implementation.

     • Time Constraints: Inquiry-based learning often requires extended periods for student exploration and investigation, which can be challenging to fit within strict schedules or limited instructional time.

     • Information Overload: Inexperienced students may struggle to manage the vast amount of information available during inquiry-based learning, leading to confusion or difficulty in synthesizing and organizing information effectively.

  3. Collaborative Learning:

     • Unequal Contributions: In collaborative learning, some students may contribute more or less than others, leading to imbalances in workload and potential frustration among group members.

     • Conflict Resolution: Collaborative settings may present challenges related to conflicts or disagreements among students. Teachers must facilitate effective communication, conflict resolution, and equitable participation.

     • Individual Accountability: Ensuring that each student is accountable for their learning and actively engaged in group activities can be challenging in collaborative learning environments.

  4. Flipped Classroom:

     • Access to Resources: Students must have reliable access to technology and resources outside of the classroom to engage with flipped learning materials, which can pose challenges in areas with limited connectivity or insufficient resources.

     • Self-Directed Learning: Flipped classrooms require students to take responsibility for their learning outside of class, which may not suit all students or require additional support for those who struggle with self-directed learning.

     • Monitoring Progress: Teachers need effective strategies to monitor student engagement with pre-class materials and identify students who may require additional support or clarification

  5. Game-Based Learning:

     • Alignment with Learning Objectives: It's crucial to ensure that game-based learning activities align with specific learning objectives and standards. Some games may be engaging but may not effectively address the intended learning outcomes.

     • Overemphasis on Fun: While games can enhance motivation, there is a risk of focusing too much on entertainment value, potentially overshadowing the learning objectives and leading to a superficial understanding of the subject matter.

     • Limited Content Coverage: Game-based learning activities may cover a specific subset of computer science concepts or skills, and care must be taken to ensure that a broader range of content is addressed alongside game-based activities.

  Understanding these potential downsides can help educators proactively address challenges and design instructional strategies that mitigate these issues. Implementing pedagogies effectively requires thoughtful planning, ongoing monitoring, and adjustments to ensure optimal learning outcomes.

6. How could teachers make computer science classes more equitable?

• Bard

  Here are some ways that teachers can make computer science classes more equitable:

  • Use a variety of teaching methods: There is no one-size-fits-all approach to teaching computer science. Teachers should use a variety of teaching methods to reach all students, including project-based learning, problem-based learning, and experiential learning.

  • Provide individualized support: Not all students learn at the same pace or in the same way. Teachers should provide individualized support to students who need it, such as extra help during office hours or after school.

  • Use culturally relevant examples: When teaching computer science concepts, teachers should use examples that are relevant to the students' cultures and experiences. This will help students to see themselves in the material and make it more meaningful to them.

  • Create a welcoming environment: Computer science can be a male-dominated field. Teachers should create a welcoming environment for all students, regardless of their gender, race, or ethnicity. This can be done by using inclusive language, displaying posters of women and underrepresented minorities in computer science, and encouraging students to share their ideas and experiences.

  • Be a role model: Teachers can be role models for their students by demonstrating their own interest in computer science. They can do this by talking about their own experiences with computer science, sharing their favorite computer science projects, and encouraging students to pursue their own interests in computer science.

  By following these tips, teachers can make computer science classes more equitable and accessible to all students.

  Here are some additional tips for making computer science classes more equitable:

  • Be aware of your own biases: Teachers should be aware of their own biases and how they might be affecting their teaching. For example, teachers who are not familiar with coding might be more likely to call on students who they think are good at coding, even if those students are not from underrepresented groups.

  • Create a space for students to share their experiences: Teachers should create a space where students feel comfortable sharing their experiences with computer science. This can be done by asking students to share their favorite computer science projects or by inviting students to lead discussions about computer science topics.

  • Provide opportunities for students to collaborate: Students from underrepresented groups are more likely to collaborate with other students from underrepresented groups. Teachers can provide opportunities for students to collaborate by assigning group projects or by creating a computer science club.

  • Celebrate student successes: Teachers should celebrate student successes, both big and small. This will help to motivate students and make them feel like they belong in the computer science classroom.

  By following these tips, teachers can create a more equitable and inclusive computer science classroom.

• ChatGPT

  To make computer science classes more equitable, teachers can implement several strategies to ensure that all students have equal access, opportunities, and support in their learning journey. Here are some approaches that can promote equity in computer science education:

  1. Inclusive Curriculum and Instruction:

     • Select diverse and inclusive learning resources, materials, and examples that represent a range of cultural perspectives and backgrounds.

     • Incorporate projects, coding exercises, and problem-solving tasks that relate to students' interests and experiences, ensuring relevance and engagement for all learners.

     • Use multiple instructional strategies and modalities to accommodate diverse learning styles and preferences.

     • Provide clear and structured instructions to support students who may require additional guidance or clarification.

  2. Addressing Stereotypes and Bias:

     • Challenge stereotypes and biases associated with computer science by highlighting the achievements and contributions of diverse individuals in the field.

     • Foster a classroom culture that values and respects diverse perspectives, encouraging open discussions and creating a safe space for all students to participate.

     • Discuss the importance of inclusivity and diversity in computer science and emphasize that anyone can succeed in the field regardless of their gender, race, or background.

  3. Access and Infrastructure:

     • Ensure equitable access to technology and resources by providing opportunities for students to use computer science tools and software, whether at school or through loan programs for home use.

     • Address the digital divide by collaborating with community organizations or leveraging government initiatives to provide internet connectivity and devices to students who lack access at home.

     • Offer flexible options for completing computer science assignments, considering students' access to technology and accommodating different learning environments.

  4. Supportive Classroom Environment:

     • Create a positive and inclusive classroom climate where all students feel valued, respected, and supported.

     • Encourage collaboration and teamwork, promoting a sense of belonging and fostering peer support among students.

     • Provide individualized support and differentiated instruction, taking into account students' diverse learning needs, backgrounds, and prior knowledge.

     • Offer additional resources, tutorials, or mentorship opportunities to students who may need extra support or encouragement.

  5. Community Engagement and Outreach:

     • Establish partnerships with community organizations, businesses, and professionals in the field of computer science to provide mentorship opportunities, guest speakers, or career exploration experiences.

     • Organize field trips, hackathons, or coding competitions that involve diverse student populations and encourage participation from underrepresented groups.

     • Advocate for computer science education equity at the school and district levels, raising awareness and influencing policy decisions to ensure equitable resource allocation and access to opportunities.

  By implementing these strategies, teachers can create an inclusive and equitable learning environment that supports all students in their computer science education journey, regardless of their background or identity. It is important to continuously reflect on and adapt teaching practices to meet the evolving needs of students and foster equitable outcomes in computer science education.

7. Why should all students learn computer science?

• Bard

  Here are some reasons why all students should learn computer science:

  • Computer science is a foundational skill: Computer science is a foundational skill that is essential for success in today's world. Computers are used in almost every aspect of our lives, from work to school to entertainment. By learning computer science, students can develop the skills they need to be successful in the 21st century.

  • Computer science is a creative field: Computer science is a creative field that allows students to express themselves and solve problems in new and innovative ways. Students can use computer science to create their own games, apps, and websites. They can also use computer science to solve real-world problems, such as climate change or poverty.

  • Computer science is a problem-solving field: Computer science is a problem-solving field that teaches students how to think logically and creatively. Students learn how to break down problems into smaller steps, identify patterns, and use algorithms to solve problems. These skills are essential for success in any field.

  • Computer science is a collaborative field: Computer science is a collaborative field that teaches students how to work effectively with others. Students learn how to communicate their ideas, give and receive feedback, and work together to achieve common goals. These skills are essential for success in any workplace.

  • Computer science is a fun field: Computer science is a fun field that allows students to learn in a hands-on and engaging way. Students can build their own games, apps, and websites. They can also work on real-world projects, such as developing software for a nonprofit organization.

  By learning computer science, students can develop the skills they need to be successful in today's world. They can also develop their creativity, problem-solving skills, and collaboration skills. Computer science is a fun and engaging field that can be enjoyed by students of all ages.

• ChatGPT

  There are several compelling reasons why all students should learn computer science:

  1. Digital Literacy and Essential Skills: In our increasingly digital world, computer science literacy has become an essential skill set. By learning computer science, students develop a deeper understanding of how technology works, become proficient in using digital tools, and enhance their overall digital literacy. These skills are crucial for everyday life, academic success, and future careers.

  2. Problem-Solving and Critical Thinking: Computer science education fosters problem-solving skills and critical thinking abilities. Students learn to break down complex problems into smaller components, analyze data, and develop systematic approaches to finding solutions. These skills are applicable beyond computer science and can be utilized in various domains and disciplines.

  3. Career Opportunities: Computer science-related careers are in high demand and offer significant opportunities for employment and innovation. By learning computer science, students open doors to diverse and rewarding career paths in fields such as software development, data analysis, cybersecurity, artificial intelligence, and more. A solid foundation in computer science can provide students with a competitive edge in the job market.

  4. Creativity and Innovation: Computer science nurtures creativity and innovation. Students have the opportunity to explore their imagination and design and create their own digital products, games, applications, or websites. Computer science education encourages students to think creatively, problem-solve, and develop innovative solutions to real-world challenges.

  5. Computational Thinking and Algorithmic Mindset: Computer science education promotes computational thinking, which involves breaking down problems into logical steps, creating algorithms, and designing efficient solutions. These skills enhance students' analytical abilities and their ability to tackle complex problems across various domains.

  6. Empowering Digital Citizenship: Computer science education empowers students to become responsible digital citizens. They learn about ethical considerations, privacy, security, and digital rights, enabling them to navigate the digital landscape safely and make informed decisions in an increasingly interconnected world.

  7. Interdisciplinary Connections: Computer science intersects with various disciplines, including mathematics, science, engineering, and the arts. By integrating computer science into education, students can explore connections between different subject areas and gain a more holistic understanding of how these disciplines intertwine and influence one another.

  8. Technological Literacy and Adaptability: Learning computer science equips students with the ability to adapt to technological advancements. As technology continues to evolve rapidly, being technologically literate and adaptable becomes increasingly important for individuals to thrive in their personal and professional lives.

  9. Addressing Equity and Access: Ensuring that all students have access to computer science education helps bridge the digital divide and promotes equity. Computer science education empowers students from diverse backgrounds to engage with technology, fostering inclusivity and reducing disparities in opportunities and outcomes.

  By providing all students with the opportunity to learn computer science, we equip them with essential skills, empower their creativity and problem-solving abilities, and prepare them for a future that is heavily influenced by technology. Computer science education is not only about creating future programmers but about fostering digital literacy, critical thinking, and empowering students to thrive in a rapidly evolving world.

- Should someone learn computer science if they are not going to be a professional computer scientist?

• Bard

  Yes, even if you are not planning to become a professional computer scientist, there are still many benefits to learning computer science.

  Here are a few reasons why you should learn computer science:

  • Computer science is a foundational skill. In today's world, computers are used in almost every aspect of our lives, from work to school to entertainment. By learning computer science, you can develop the skills you need to be successful in the 21st century.

  • Computer science is a creative field. Computer science is a creative field that allows you to express yourself and solve problems in new and innovative ways. You can use computer science to create your own games, apps, and websites. You can also use computer science to solve real-world problems, such as climate change or poverty.

  • Computer science is a problem-solving field. Computer science is a problem-solving field that teaches you how to think logically and creatively. You learn how to break down problems into smaller steps, identify patterns, and use algorithms to solve problems. These skills are essential for success in any field.

  • Computer science is a collaborative field. Computer science is a collaborative field that teaches you how to work effectively with others. You learn how to communicate your ideas, give and receive feedback, and work together to achieve common goals. These skills are essential for success in any workplace.

  • Computer science is a fun field. Computer science is a fun field that allows you to learn in a hands-on and engaging way. You can build your own games, apps, and websites. You can also work on real-world projects, such as developing software for a nonprofit organization.

  Even if you don't want to be a professional computer scientist, learning computer science can still help you in your career. Many jobs now require some level of computer skills, and learning computer science can give you a competitive edge. Additionally, computer science can help you develop critical thinking, problem-solving, and communication skills that are valuable in any field.

  If you're interested in learning more about computer science, there are many resources available online and in libraries. You can also take classes at a local community college or university. With a little effort, you can learn the basics of computer science and start using these skills to improve your life.

• ChatGPT

  Yes, learning computer science can be beneficial even if someone doesn't plan to become a professional computer scientist. Computer science education provides a valuable set of skills and knowledge that can be applied in various aspects of life and different career paths. Here are some reasons why learning computer science is beneficial, even outside of a professional computer science career:

  1. Problem-Solving Skills: Computer science fosters problem-solving abilities and critical thinking. These skills are valuable in any field or profession, as they enable individuals to approach challenges systematically, analyze data, and develop effective solutions.

  2. Digital Literacy: In our digital age, having a strong foundation in computer science and technology is essential. Learning computer science helps individuals become digitally literate, allowing them to navigate and utilize technology effectively in their personal and professional lives.

  3. Automation and Efficiency: Understanding computer science principles can help individuals automate repetitive tasks, streamline processes, and increase efficiency in various domains. This can be valuable in any profession, from finance to healthcare, from marketing to education.

  4. Interdisciplinary Connections: Computer science intersects with numerous disciplines, such as mathematics, science, engineering, and the arts. Learning computer science can foster interdisciplinary thinking and help individuals understand how technology is integrated into different fields, creating opportunities for innovation and collaboration.

  5. Computational Thinking: Computer science education promotes computational thinking, which involves breaking down complex problems, developing algorithms, and designing efficient solutions. Computational thinking skills are transferable and can be applied to various situations, helping individuals approach challenges with a logical and structured mindset.

  6. Digital Creativity: Computer science offers opportunities for digital creativity and expression. Learning computer science can enable individuals to develop their own digital projects, such as websites, mobile apps, or multimedia presentations, fostering creativity and innovation in various contexts.

  7. Technology Management: Having a foundational understanding of computer science allows individuals to make informed decisions about technology use and management. This includes considerations such as data privacy, cybersecurity, and ethical implications, which are important in today's digital world.

  8. Adaptability and Future Readiness: Technology is rapidly evolving, and being knowledgeable about computer science prepares individuals for the changing landscape. Learning computer science equips individuals with adaptable skills and a mindset that allows them to navigate and embrace emerging technologies.

  In summary, learning computer science can provide valuable skills, knowledge, and perspectives that extend beyond a specific computer science career. It equips individuals with problem-solving abilities, digital literacy, interdisciplinary connections, computational thinking skills, and a readiness to navigate a technology-driven world. Regardless of one's career path, computer science education offers benefits that can enhance personal and professional growth in today's digital age.

- What prevents students from being interested in computer science?

• Bard

  There are many factors that can prevent students from being interested in computer science. Some of these factors include:

  • Perception that computer science is difficult: Many students have the perception that computer science is a difficult subject. This perception can be due to a number of factors, including the fact that computer science is often taught in a theoretical way, and that it can be challenging to learn how to code.

  • Lack of female role models: Computer science is a male-dominated field. This can make it difficult for female students to see themselves as computer scientists, and can discourage them from pursuing a career in the field.

  • Lack of diversity: Computer science is also a field that is lacking in diversity. This can make it difficult for students from underrepresented groups to find role models and mentors in the field, and can make them feel like they don't belong.

  • Negative stereotypes: There are a number of negative stereotypes about computer science, such as the stereotype that it is a boring and solitary field. These stereotypes can discourage students from pursuing a career in computer science.

  There are a number of things that can be done to address these factors and to encourage more students to be interested in computer science. Some of these things include:

  • Teaching computer science in a more engaging way: Computer science can be taught in a more engaging way by using hands-on activities, games, and projects. This will help students to see the relevance of computer science to their lives, and will make it more fun to learn.

  • Providing more female role models: There are a number of organizations that are working to provide more female role models in computer science. These organizations are working to increase the number of female faculty in computer science departments, and to provide more opportunities for female students to get involved in computer science activities.

  • Promoting diversity: There are a number of organizations that are working to promote diversity in computer science. These organizations are working to increase the number of students from underrepresented groups who are studying computer science, and to create a more welcoming environment for these students.

  • Challenging negative stereotypes: There are a number of ways to challenge negative stereotypes about computer science. One way is to talk to students about the many different ways that computer science can be used to solve real-world problems. Another way is to highlight the many successful people in computer science who come from a variety of backgrounds.

  By addressing these factors, we can encourage more students to be interested in computer science. This will help to ensure that the field of computer science is more representative of the population, and that it is able to benefit from the diversity of thought and experience that all students have to offer.

• ChatGPT

  Several factors can influence students' lack of interest or reluctance to pursue computer science. Here are some common barriers that can prevent students from being interested in computer science:

  1. Lack of Awareness: Many students may not be aware of the opportunities and potential of computer science careers. Limited exposure to computer science concepts and its real-world applications can result in a lack of understanding and interest in the field.

  2. Stereotypes and Perceived Incompatibility: Stereotypes surrounding computer science, such as it being "only for geeks" or "male-dominated," can discourage certain students from exploring the field. Some students may perceive computer science as too technical or unrelated to their personal interests and passions.

  3. Limited Role Models and Representation: The underrepresentation of diverse role models in computer science can create a perception that certain groups, such as women or minority students, may not belong or succeed in the field. Lack of representation can discourage students from envisioning themselves pursuing computer science careers.

  4. Inadequate Preparation: Insufficient exposure to computer science education at an early age or limited access to quality computer science programs can hinder students' interest. Without exposure to foundational concepts and hands-on experiences, students may not fully grasp the potential and relevance of computer science.

  5. Preconceived Notions of Difficulty: Some students may perceive computer science as a difficult subject, particularly if they have had limited exposure to it or faced challenges in related areas like mathematics. This perception can create a barrier to entry and discourage students from exploring the field.

  6. Lack of Engaging and Relevant Curriculum: Outdated or uninspiring curriculum materials can fail to capture students' interest or fail to connect computer science concepts to real-world applications. If the curriculum does not align with students' interests or fails to engage them, it can dampen their enthusiasm for the subject.

  7. Gender and Cultural Biases: Gender and cultural biases can contribute to the underrepresentation of certain groups in computer science. Societal biases, stereotypes, and cultural expectations can influence students' perception of their own abilities and interest in the field.

  8. Limited Access to Resources and Opportunities: Inequitable access to computer science resources, such as technology, quality instruction, and extracurricular activities, can hinder students' engagement and interest. Lack of access can create disparities in opportunity, particularly for students from underprivileged or marginalized communities.

  To address these barriers and promote students' interest in computer science, it is important to raise awareness about the field, provide diverse role models, offer engaging and inclusive curriculum materials, provide early and equitable access to computer science education, and create supportive and inclusive learning environments. By addressing these factors, we can help students overcome barriers and cultivate a genuine interest and enthusiasm for computer science.

8. How can teachers learn how to teach computer science classes if they don't have a background in computer science?

• Bard

  There are many ways for teachers to learn how to teach computer science classes if they don't have a background in computer science. Here are a few suggestions:

  • Take a computer science class: This is a great way to learn the basics of computer science and to get a feel for how the subject is taught. There are many online and in-person computer science classes available, and many of them are designed specifically for teachers.

  • Read computer science books and articles: There are many great books and articles available that can help teachers learn about computer science. Some of these resources are written specifically for teachers, while others are written for a general audience.

  • Attend computer science conferences and workshops: There are many conferences and workshops held throughout the year that focus on computer science education. These events can be a great way to learn about new teaching methods and to network with other teachers who are interested in computer science.

  • Connect with other teachers who are teaching computer science: There are many online and in-person communities for teachers who are teaching computer science. These communities can be a great way to get support and advice from other teachers who are facing similar challenges.

  • Use online resources: There are many online resources available that can help teachers learn how to teach computer science. Some of these resources include Khan Academy, Code.org, and Scratch.

  By taking advantage of these resources, teachers can learn how to teach computer science even if they don't have a background in the subject.

  Here are some additional tips for teachers who are learning how to teach computer science:

  • Start small: Don't try to teach everything at once. Start with the basics, such as learning about algorithms and data structures. Once you have a good understanding of the basics, you can start teaching more advanced concepts.

  • Use hands-on activities: Students learn best by doing. Use hands-on activities to help students learn about computer science concepts. For example, you can have students build their own websites or create their own games.

  • Be patient: It takes time to learn how to teach computer science. Don't get discouraged if you don't get it right away. Keep practicing, and you'll eventually get the hang of it.

  With a little effort, teachers can learn how to teach computer science and help their students develop the skills they need to be successful in the 21st century.

• ChatGPT

  If teachers don't have a background in computer science but want to learn how to teach computer science classes, there are several avenues they can explore to gain the necessary knowledge and skills. Here are some options:

  1. Professional Development Workshops and Courses: Teachers can attend professional development workshops or enroll in courses specifically designed to provide training in teaching computer science. These programs are often offered by educational organizations, universities, or online platforms and can cover topics such as curriculum development, pedagogical approaches, and programming concepts.

  2. Online Resources and Tutorials: There are numerous online resources and tutorials available that can help teachers learn computer science concepts and programming languages. Websites like Code.org, CSUnplugged, and Scratch offer free resources, lesson plans, and interactive platforms that cater to educators without a computer science background.

  3. Collaboration and Networking: Teachers can connect with other educators who have experience teaching computer science and seek their guidance and support. Joining online communities, forums, or social media groups focused on computer science education can provide opportunities for collaboration, sharing resources, and learning from experienced educators.

  4. Partnerships and Mentoring: Collaborating with computer science professionals or organizations can be beneficial. Establishing partnerships with local businesses, universities, or industry professionals can provide opportunities for mentorship, guest speakers, or access to additional resources. These partnerships can enhance teachers' understanding of the field and provide real-world context for teaching computer science.

  5. Curriculum and Resources Alignment: Teachers can leverage existing computer science curriculum frameworks and standards, such as those provided by national or state education agencies. Aligning their instruction with established frameworks ensures they cover essential concepts and learning objectives, even without a deep computer science background.

  6. Peer Learning and Communities of Practice: Collaborating with colleagues within the same school or district who have experience teaching computer science can foster a supportive learning environment. Teachers can participate in study groups, share lesson plans, and engage in peer observation and feedback to enhance their teaching practice.

  7. Continuous Learning and Reflection: Teaching computer science is a dynamic field, and teachers should embrace continuous learning and reflection. Engaging in self-study, attending conferences or webinars, and staying updated with the latest developments in computer science education can help teachers refine their knowledge and teaching strategies.

  It's important for teachers without a computer science background to approach their teaching with enthusiasm, a growth mindset, and a willingness to learn alongside their students. By leveraging available resources, seeking professional development opportunities, and building supportive networks, teachers can develop the necessary skills and confidence to effectively teach computer science classes.

9. What is computational thinking and why is it being taught in schools?

• Bard

  Computational thinking is a problem-solving approach that uses abstraction, decomposition, pattern recognition, algorithms, and evaluation. It is a way of thinking that can be used to solve problems in any domain, not just computer science.

  Computational thinking is being taught in schools because it is a valuable skill for students to have in the 21st century. In a world where technology is increasingly pervasive, computational thinking skills can help students to:

  • Understand how computers work

  • Solve problems creatively and efficiently

  • Communicate effectively about technology

  • Be more engaged in their learning

  Computational thinking can be taught in a variety of ways. Some common methods include:

  • Problem-based learning: Students are presented with a problem to solve, and they use computational thinking skills to come up with a solution.

  • Project-based learning: Students work on a long-term project that requires them to use computational thinking skills.

  • Games: Students can learn computational thinking skills by playing games that involve problem-solving and creativity.

  • Online resources: There are many online resources available that can help students learn computational thinking.

  Computational thinking is a valuable skill for students to have in the 21st century. It can help students to understand how computers work, solve problems creatively and efficiently, communicate effectively about technology, and be more engaged in their learning.

  Here are some of the benefits of teaching computational thinking in schools:

  • Improved problem-solving skills: Computational thinking teaches students how to break down problems into smaller steps, identify patterns, and use algorithms to solve problems. These skills can be used to solve problems in any domain, not just computer science.

  • Increased creativity: Computational thinking encourages students to think outside the box and come up with new and innovative solutions to problems.

  • Improved communication skills: Computational thinking requires students to communicate their ideas clearly and concisely. This can help them to be more effective communicators in all areas of their lives.

  • Increased engagement: Computational thinking is a hands-on, engaging approach to learning that can help students stay motivated and interested in their studies.

  By teaching computational thinking in schools, we can help students develop the skills they need to be successful in the 21st century.

• ChatGPT

  Computational thinking is a problem-solving approach inspired by the principles used in computer science. It involves breaking down complex problems into smaller, more manageable components, formulating algorithms or step-by-step procedures to solve them, and utilizing logical and systematic thinking to arrive at solutions. Computational thinking encompasses a set of skills and strategies that are transferable across various domains, not just limited to computer science.

  Here are some key aspects of computational thinking:

  1. Decomposition: Breaking down complex problems into smaller, more manageable parts or subproblems.

  2. Pattern Recognition: Identifying patterns, similarities, and regularities within problems or data.

  3. Abstraction: Extracting the essential features of a problem, disregarding unnecessary details, and focusing on the core elements.

  4. Algorithmic Design: Developing step-by-step instructions or algorithms to solve problems systematically.

  5. Algorithmic Evaluation: Analyzing and assessing the efficiency and effectiveness of algorithms and solutions.

  Computational thinking is being taught in schools for several reasons:

  1. Problem-Solving Skills: Computational thinking equips students with problem-solving strategies that can be applied across various disciplines. It enhances their ability to tackle complex problems, analyze data, and develop systematic approaches to finding solutions.

  2. Digital Literacy and Technological Understanding: In an increasingly technology-driven world, computational thinking provides students with a deeper understanding of how technology works, fostering digital literacy and empowering them to navigate and utilize technology effectively.

  3. Preparation for Future Careers: Computational thinking skills are highly valuable in today's job market, regardless of whether students pursue computer science as a career. These skills can be applied in fields such as engineering, mathematics, science, business, healthcare, and more, preparing students for a wide range of future career paths.

  4. Critical and Logical Thinking: Computational thinking encourages critical thinking, logical reasoning, and analytical skills. It helps students approach problems systematically, consider multiple perspectives, and evaluate the effectiveness of their solutions.

  5. Creativity and Innovation: Computational thinking nurtures creativity and innovation by challenging students to think outside the box, design algorithms, and develop novel solutions. It fosters a mindset that encourages experimentation, iteration, and the exploration of multiple possibilities.

  6. Integration with Core Subjects: Computational thinking can be integrated with other subjects, such as mathematics, science, and engineering, enhancing students' understanding and application of concepts in those areas. It provides a cross-curricular approach that enriches learning experiences.

  7. Future-Proofing Students: Teaching computational thinking prepares students for a rapidly evolving future. It equips them with adaptable skills, such as problem-solving, logical thinking, and algorithmic design, which are essential in an increasingly technology-driven society.

  By incorporating computational thinking into education, schools aim to develop students' problem-solving abilities, digital literacy, critical thinking skills, and readiness to tackle the challenges of the 21st century. It provides a foundation for students to navigate the complexities of an increasingly digital world and prepares them for a wide range of academic and career opportunities.

- List five examples of computational thinking

• Bard

  Here are five examples of computational thinking:

  1. Decomposition: Breaking down a problem into smaller, more manageable steps. For example, if you were trying to program a robot to walk, you might break the problem down into steps like moving the legs, moving the arms, and balancing the body.

  2. Pattern recognition: Identifying patterns in data or information. For example, if you were trying to predict the weather, you might look for patterns in historical weather data.

  3. Abstraction: Creating a simplified representation of a problem or system. For example, if you were trying to design a new website, you might create a wireframe, which is a simplified drawing of the website's layout.

  4. Algorithms: Developing a step-by-step process for solving a problem. For example, if you were trying to write a program that sorts a list of numbers, you would need to develop an algorithm that tells the computer how to compare the numbers and put them in order.

  5. Evaluation: Assessing the success of a solution to a problem. For example, if you were trying to program a robot to walk, you would need to evaluate whether the robot is able to walk successfully.

  These are just a few examples of computational thinking. Computational thinking is a broad and complex topic, and there are many different ways to approach it. However, the five examples listed above provide a good starting point for understanding what computational thinking is and how it can be used to solve problems.

• ChatGPT

  Certainly! Here are five examples of computational thinking:

  1. Creating a Recipe: When creating a recipe, computational thinking can be applied by breaking down the cooking process into smaller steps, identifying patterns or sequences, and designing an algorithmic set of instructions that ensure a successful outcome.

  2. Solving a Sudoku Puzzle: Computational thinking can be used to solve Sudoku puzzles by breaking down the problem into smaller components, recognizing patterns and constraints, and systematically applying logical rules to fill in the missing numbers.

  3. Planning a Route: When planning a route to reach a destination, computational thinking involves decomposing the problem into smaller steps, analyzing available routes, considering factors such as distance and traffic patterns, and selecting the most efficient path based on these considerations.

  4. Developing an App: In app development, computational thinking is applied by decomposing the app's functionality into smaller modules, designing algorithms and flowcharts to define how the app will function, and implementing logical structures to ensure the app operates as intended.

  5. Conducting Data Analysis: Computational thinking plays a vital role in data analysis by breaking down complex data sets into manageable segments, identifying patterns or trends within the data, developing algorithms to process and analyze the data, and drawing meaningful conclusions based on the results.

  In each of these examples, computational thinking involves problem decomposition, pattern recognition, abstraction, algorithmic design, and evaluation of solutions, showcasing how this approach can be applied to various scenarios beyond computer science itself.

10. Write me a lesson that teaches an elementary student how to create a short Scratch.mit.edu story

• Bard

  Sure, here is a lesson that teaches an elementary student how to create a short Scratch story:

  Lesson Objective:

  • Students will be able to create a short Scratch story.

  Materials:

  • A computer with Scratch installed

  • A pencil and paper

  Procedure:

  1. Introduction:

     • Introduce Scratch to students and explain that it is a programming language that can be used to create interactive stories, games, and animations.

     • Show students some examples of Scratch projects that have been created by other students.

  2. Brainstorming:

     • Have students brainstorm ideas for their own Scratch stories.

     • Encourage them to think about characters, settings, and plots.

  3. Creating the storyboard:

     • Once students have some ideas, have them create a storyboard for their project.

     • A storyboard is a series of drawings that show the sequence of events in a story.

     • This will help students to visualize their story and to plan out the code that they will need to write.

  4. Coding:

     • Once students have a storyboard, they can start coding their project.

     • Scratch has a drag-and-drop interface that makes it easy for beginners to code.

     • There are also many tutorials available online that can help students learn how to code in Scratch.

  5. Testing:

     • Once students have finished coding their project, they should test it to make sure that it works correctly.

     • They can do this by running the project and seeing if it plays the way they expect it to.

  6. Sharing:

     • Once students are happy with their project, they can share it with others.

     • They can do this by publishing their project on the Scratch website or by sharing it with friends and family.

  Assessment:

  • Students will be assessed on their ability to create a short Scratch story.

  • They will be evaluated on their creativity, their coding skills, and their ability to follow instructions.

  Differentiation:

  • For students who are struggling, provide them with more support and scaffolding.

  • This could include providing them with more detailed instructions, breaking the project down into smaller steps, or working with them one-on-one.

  • For students who are ahead, challenge them to create more complex projects.

  • This could include adding more characters, settings, or plots to their story.

  Extensions:

  • Once students have learned the basics of Scratch, they can explore other features of the program.

  • This could include using sounds, music, and animations in their projects.

  • Students can also collaborate with other students to create more complex projects.

  Conclusion:

  • Scratch is a great way for elementary students to learn about coding and to create their own interactive stories, games, and animations.

  • It is a fun and engaging way to learn, and it can help students to develop their creativity, problem-solving skills, and critical thinking skills.

• ChatGPT

  Title: Creating a Story with Scratch!

  Objective: By the end of this lesson, students will be able to create a short interactive story using Scratch.mit.edu.

  Materials:

  • Computers or devices with internet access

  • Scratch.mit.edu (accessible online)

  Duration: 1-2 class periods (approx. 45-60 minutes each)

  Lesson Steps:

  1. Introduction (10 minutes):

     • Begin the lesson by discussing the concept of storytelling and its importance.

     • Share examples of interactive stories or games created with Scratch to spark students' interest and show what they can create.

     • Explain that today, students will have the opportunity to create their own interactive story using Scratch.

  2. Scratch Overview (5 minutes):

     • Familiarize students with the Scratch interface and its basic features.

     • Show them how to access Scratch.mit.edu and introduce the different components, such as the stage, sprites, and blocks.

     • Highlight key blocks they will use, such as "when green flag clicked," "say," and "wait."

  3. Brainstorming and Planning (10 minutes):

     • Encourage students to brainstorm story ideas individually or in pairs. They can consider characters, settings, and a simple plot.

     • Provide guidance and support as students come up with their story concepts.

     • Have students sketch or write a brief outline of their story, including key scenes or actions.

  4. Creating the Story (20 minutes):

     • Guide students through the process of creating their story using Scratch:

     • Start by helping them choose or create sprite characters that fit their story.

     • Show them how to add backgrounds and how to switch between different scenes.

     • Introduce basic programming concepts such as "when green flag clicked" and "broadcast," which can be used to trigger actions and change scenes.

     • Demonstrate how to use blocks like "say" and "wait" to add dialogue and timing to the story.

  5. Adding Interactivity (10 minutes):

     • Explain the concept of interactivity and how it can enhance their story.

     • Introduce events like mouse clicks or key presses that can trigger specific actions.

     • Encourage students to think about how they can make their story more engaging and interactive, such as allowing the user to make choices that affect the plot.

  6. Testing and Iterating (10 minutes):

     • Emphasize the importance of testing and iterating their story.

     • Instruct students to play through their story, checking for any errors or areas that need improvement.

     • Encourage them to make adjustments, add details, or refine the storytelling elements based on their observations.

  7. Sharing and Reflection (10 minutes):

     • Provide time for students to share their completed stories with their peers.

     • As a class, reflect on the process and discuss what they enjoyed, challenges they faced, and any lessons learned.

     • Celebrate their accomplishments and offer constructive feedback to encourage further growth.

  Extensions:

  • Encourage students to add sound effects or background music to their stories.

  • Explore additional Scratch features like costumes, variables, or custom blocks for more advanced storytelling options.

  • Invite students to write a script for their story to enhance their writing skills alongside the visual and programming aspects.

  Note: Adjust the duration and complexity of the lesson based on the grade level and familiarity of students with Scratch.

- Write me a lesson that teaches an elementary student how to create a short Scratch.mit.edu story about algorithmic bias

• Bard

  I'm unable to help, as I am only a language model and don't have the ability to process and understand that.

• ChatGPT

  Title: Exploring Algorithmic Bias through Scratch Storytelling

  Objective: By the end of this lesson, students will be able to create a short interactive story using Scratch.mit.edu to explore the concept of algorithmic bias.

  Materials:

  • Computers or devices with internet access

  • Scratch.mit.edu (accessible online)

  • Duration: 2-3 class periods (approx. 45-60 minutes each)

  Lesson Steps:

  1. Introduction (15 minutes):

     • Start by engaging students in a discussion about fairness and bias. Ask questions like, "Have you ever felt treated unfairly by a decision made by a computer or app?"

     • Introduce the concept of algorithmic bias, explaining that it occurs when computer algorithms produce unfair or discriminatory outcomes.

     • Show examples of algorithmic bias in real-life scenarios, such as biased facial recognition systems or gender-biased job application algorithms.

  2. Brainstorming and Planning (20 minutes):

     • Guide students to brainstorm story ideas related to algorithmic bias. Encourage them to think about scenarios where bias can occur and its impact on individuals or communities.

     • Have students work individually or in pairs to develop a brief outline of their story, considering the characters, settings, and plot elements that will address the concept of algorithmic bias.

  3. Creating the Story (40 minutes):

     • Introduce students to the Scratch interface and guide them through creating their interactive story:

     • Help them select or create sprite characters and backgrounds that align with their story's theme.

     • Show them how to use blocks to move characters, trigger events, and display dialogue.

     • Encourage them to include multiple scenes or levels that illustrate different instances of algorithmic bias.

  4. Incorporating Algorithmic Bias (30 minutes):

     • Guide students in integrating algorithmic bias into their story:

     • Discuss different forms of bias, such as gender, race, or age, and how they can be portrayed in their story.

     • Help students design scenes or challenges where the characters face biased outcomes due to an algorithm's unfair decision-making.

     • Encourage them to think critically about the consequences of bias and its impact on individuals or communities.

  5. Testing and Iterating (20 minutes):

     • Instruct students to play through their stories and test for any programming errors or areas that need improvement.

     • Encourage them to adjust dialogue, character movements, or scene transitions to ensure clarity and engagement.

     • Emphasize the importance of iteration and refining their story based on feedback.

  6. Sharing and Reflection (15 minutes):

     • Provide an opportunity for students to share their completed stories with their peers.

     • Encourage discussions about the portrayal of algorithmic bias and the potential effects on different characters in each story.

     • Facilitate a reflection session where students discuss the challenges faced and lessons learned while creating their stories.

  Extensions:

  • Invite students to present their stories to a wider audience, such as other classes or parents, to raise awareness about algorithmic bias.

  • Encourage students to research and discuss ways to mitigate algorithmic bias and promote fair and ethical use of technology.

  • Explore additional Scratch features, such as variables or conditionals, to add complexity and depth to their stories.

  Note: Adjust the duration and complexity of the lesson based on the grade level and prior experience of students with Scratch. Additionally, ensure a supportive and inclusive learning environment by emphasizing empathy and respect when discussing bias-related topics.

- Write me a lesson that teaches an elementary student how to create a short Scratch.mit.edu story about algorithmic bias while encouraging students to incorporate their own interests, needs, desires, etc. in the story

• Bard

  Lesson Plan: Teaching Elementary Students About Algorithmic Bias Using Scratch

  Objectives:

  • Students will be able to define algorithmic bias.

  • Students will be able to identify examples of algorithmic bias in the real world.

  • Students will be able to create a short Scratch story about algorithmic bias that incorporates their own interests, needs, and desires.

  Materials:

  • Scratch software

  • Computers

  • Projector (optional)

  • Whiteboard (optional)

  Procedure:

  1. Introduce the concept of algorithmic bias.

     • Begin by asking students what they know about algorithms. Explain that algorithms are sets of instructions that computers follow to complete tasks.

     • Next, introduce the concept of algorithmic bias. Explain that algorithmic bias occurs when an algorithm produces unfair or discriminatory results.

     • Provide students with some examples of algorithmic bias in the real world. For example, you could discuss how algorithms used to predict who is likely to commit a crime have been shown to be biased against people of color.

  2. Have students brainstorm ideas for their Scratch stories.

     • Once students have a basic understanding of algorithmic bias, have them brainstorm ideas for their Scratch stories. Encourage them to think about their own interests, needs, and desires when coming up with ideas.

     • Some possible story ideas include:

     • A story about a student who is denied a loan because of their race or gender.

     • A story about a job applicant who is not hired because of their age or disability.

     • A story about a person who is targeted by online advertising because of their political beliefs.

  3. Help students create their Scratch stories.

     • Once students have come up with ideas for their stories, help them create their Scratch projects. You can provide them with guidance on how to use the Scratch software to create characters, backgrounds, and animations.

     • Encourage students to be creative and to incorporate their own unique perspectives into their stories.

  4. Share students' stories with the class.

     • Once students have completed their Scratch stories, have them share their work with the class. This will give them a chance to learn from each other and to discuss the issue of algorithmic bias.

     • You could also encourage students to post their stories online so that others can see them.

  Assessment:

  • As students are working on their Scratch stories, observe them to make sure that they are understanding the concept of algorithmic bias. You can also ask them questions about their stories to assess their understanding.

  • At the end of the lesson, have students complete a short quiz or activity to assess their knowledge of algorithmic bias.

  Differentiation:

  • For students who are struggling to understand the concept of algorithmic bias, provide them with additional support. You could provide them with simpler examples of algorithmic bias or you could break the concept down into smaller steps.

  • For students who are ready for a challenge, encourage them to create more complex Scratch stories that explore the issue of algorithmic bias in more depth. You could also challenge them to incorporate different perspectives into their stories.

• ChatGPT

  Title: Exploring Algorithmic Bias: Your Story, Your Voice

  Objective: By the end of this lesson, students will be able to create a short interactive story using Scratch.mit.edu that explores the concept of algorithmic bias while incorporating their own interests, needs, desires, or experiences.

  Materials:

  • Computers or devices with internet access

  • Scratch.mit.edu (accessible online)

  Duration: 2-3 class periods (approx. 45-60 minutes each)

  Lesson Steps:

  1. Introduction (15 minutes):

     • Begin by discussing the concept of algorithmic bias and its potential impact on people's lives.

     • Encourage students to share their thoughts, interests, needs, desires, or experiences that they would like to incorporate into their own story.

     • Explain that they will have the opportunity to create a personalized interactive story using Scratch that addresses algorithmic bias while reflecting their unique perspectives.

  2. Brainstorming and Planning (20 minutes):

     • Guide students through a brainstorming session, allowing them to explore their interests, needs, desires, or experiences that they would like to feature in their story.

     • Encourage them to consider how algorithmic bias can intersect with their chosen themes.

     • Have students develop a brief outline or storyboard that incorporates their ideas and demonstrates how algorithmic bias will be explored in their story.

  3. Creating the Story (40 minutes):

     • Introduce students to the Scratch interface and guide them through creating their interactive story:

     • Assist them in selecting or creating sprite characters, backgrounds, and other visual elements that align with their chosen themes.

     • Demonstrate how to use Scratch blocks to program character movements, trigger events, and display dialogue.

     • Encourage students to incorporate their own interests, needs, desires, or experiences into the narrative, dialogue, or challenges within the story.

  4. Addressing Algorithmic Bias (30 minutes):

     • Guide students in addressing algorithmic bias within their stories:

     • Discuss different forms of bias and how they can be reflected in the story's plot, characters, or decision-making scenarios.

     • Help students design scenes or challenges that illustrate instances of bias and the potential consequences for the characters.

     • Encourage them to think critically about the implications of algorithmic bias and how it intersects with their chosen themes.

  5. Testing and Iterating (20 minutes):

     • Instruct students to play through their stories and test for any programming errors or areas that need improvement.

     • Encourage them to gather feedback from peers and make adjustments based on the effectiveness of storytelling, clarity of messages, and the integration of algorithmic bias.

  6. Sharing and Reflection (15 minutes):

     • Provide an opportunity for students to share their completed stories with their peers.

     • Encourage discussions about the incorporation of their interests, needs, desires, or experiences and how they intersect with the exploration of algorithmic bias.

     • Facilitate a reflection session where students discuss the challenges faced, the satisfaction gained from incorporating personal elements, and the lessons learned through the creative process.

  Extensions:

  • Invite students to reflect on how their own experiences and perspectives can help shape a more inclusive and fair digital world.

  • Encourage students to share their stories beyond the classroom, such as through school assemblies, digital portfolios, or community events, to amplify their voices and promote discussions on algorithmic bias.

  • Explore additional Scratch features, such as variables, conditionals, or user input, to add complexity and depth to their stories.

  Note: Adjust the duration and complexity of the lesson based on the grade level and prior experience of students with Scratch. Foster a supportive and inclusive environment, ensuring that all students feel comfortable sharing their interests, needs, desires, or experiences while discussing the sensitive topic of algorithmic bias.

11. Based on the kinds of questions I've already asked today, what questions should I ask you next?

• Bard

  Based on the kinds of questions you've already asked today, I would recommend that you ask me more questions about:

  • Artificial intelligence (AI)

  • Large language models (LLMs)

  • Natural language processing (NLP)

  • Machine learning (ML)

  • Data science

  • Programming

  These are all topics that I am very knowledgeable about, and I would be happy to answer any questions you have about them. I can also provide you with additional resources, such as articles, blog posts, and videos, that can help you learn more about these topics.

  Here are some specific questions that you could ask me:

  • What is the difference between AI and ML?

  • How do LLMs work?

  • What are some of the applications of NLP?

  • What are some of the challenges of ML?

  • What is the future of data science?

  • What are some of the best programming languages to learn?

  I hope this helps!

• ChatGPT

  Based on your previous questions, here are some potential topics and questions you could consider asking next:

  1. Strategies for Teaching Computer Science:

     • What are some effective strategies for introducing coding concepts to elementary students?

     • How can project-based learning be incorporated into computer science education?

     • What are some resources or tools that can support hands-on learning in computer science?

  2. Ethical Considerations in Technology:

     • What are the ethical implications of algorithmic bias in various domains?

     • How can we educate students about responsible and ethical use of technology?

     • Are there any emerging ethical issues related to artificial intelligence that students should be aware of?

  3. Professional Development for Teachers:

     • What are some opportunities for teachers to enhance their knowledge and skills in teaching computer science?

     • Are there specific certifications or training programs available for educators interested in computer science education?

     • How can teachers without a computer science background receive support in teaching computer science effectively?

  4. Integrating Computer Science Across the Curriculum:

     • How can computer science concepts be integrated into subjects like math, science, or language arts?

     • Are there any interdisciplinary projects that combine computer science with other subjects?

     • What are the benefits of cross-curricular integration of computer science in elementary education?

  5. Engaging Underrepresented Groups in Computer Science:

     • What strategies can be implemented to engage and encourage girls or underrepresented groups in computer science?

     • Are there any initiatives or programs focused on promoting diversity and inclusion in computer science education?

     • How can we address the gender or diversity gap in the tech industry from an early education perspective?

  Remember, these are just suggestions based on your previous questions, and you are welcome to explore other areas of interest as well.

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Resources/Links Relevant to This Episode

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    • Recommendations for Preventing Burnout in Education (Part 2)

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    • Recommendations for Preventing Burnout in Education (Part 3)

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    • A short article on this topic

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