Alright, let's dive into this research page where everyone's talking about.
It's all about chat GPT in computer science education.
Is it a friend or a foe?
Yeah, the researchers really tried to get to the bottom of that.
What stood out to me is they didn't just ask chat GPT to write some code.
They actually designed these tests, almost like a whole computer science curriculum.
They started with foundational knowledge and went all the way up to advanced topics like,
get this, quantum computing.
Whoa, quantum computing.
So like, they put chat GPT through a CS boot camp?
Did they at least use the newest version, like, 4?
Well, actually, they used 3.5.
At the time, it was the most widely available version, so made sense for students.
Right, gotta keep it realistic.
Okay, so level one was like CS 101.
What kind of stuff did they test there?
So that was all about fundamental skills, like writing Python for common algorithms,
sorting algorithms, data structures, that kind of thing.
Okay, the building blocks.
How'd chat GPT do?
Honestly, pretty darn good.
82% of the code it generated was totally correct.
The other 18% was like, incomplete, but fixable.
Wow.
Yeah, they even gave it the bubble sort algorithm, nailed it.
First try.
But isn't that kind of dangerous?
Like, what if students just copy and paste?
They don't actually learn anything.
Absolutely, that's the big question.
Chat GPT can write the code, but students need to understand it.
Just like you can use a calculator, but you still gotta know basic math.
Right, gotta get those fundamentals down.
So chat GPT past kindergarten.
What about middle school?
Level two, core competency.
So that's where things get a little spicier.
This time, they switched to Java and focused on design patterns.
Okay, so less about just coding, more about like elegant coding.
Exactly.
Think of design patterns as solutions to common programming problems,
like blueprints for good, clean code.
Ah, so they're testing if chat GPT can write code that other humans can actually read.
Right, and they even had it do some refactoring,
like cleaning up existing code to make it better without changing how it works.
Oh, and they even had it try to write some JANIT tests.
Oh yeah, JANIT tests every developer's favorite, right?
Ah, right.
But seriously, those are super important now, aren't they?
Totally, it's like having a robot QA tester built into your code.
So did chat GPT pass this level too?
Well, it wasn't a total fail.
Most of the code was technically correct, but it wasn't very pretty.
What do you mean?
Like it would cram everything into one giant file,
which is like a huge no-no for organization.
Ah, so it worked, but it would give any developer a headache looking at it.
Pretty much.
They also found a lot of code smells.
Code smells.
It's like not an actual error,
but a sign that the code might be hard to maintain or understand.
Hmm, interesting.
Okay, and what about the JANIT tests?
Did those at least work?
Not really.
Sometimes they wouldn't even compile,
and often they didn't really test what they were supposed to.
Yikes.
So not exactly a shining example for students learning how to write tests.
Nope, definitely not.
You got to know what you're doing to evaluate chat GPT's output.
So for level two, it sounds like chat GPT needs a good tutor itself.
Yeah, more like a study partner, not a teacher.
All right, well, before we move on to the final boss, quantum computing,
what was the researcher's overall take on chat GPT at this intermediate level?
Well, they saw potential, especially for exploring different ideas and getting started,
but they were pretty clear.
It's not ready to replace a human programmer just yet.
Got it.
Still need some work.
Yeah.
All right, let's crank up the difficulty.
What did they throw at chat GPT at level three?
Okay, hold on tight, because for this one, they went full on quantum.
Quantum computing.
That's like next level stuff, right?
Well, yeah, definitely advanced.
You got to know your physics and your computer science for this one.
So what exactly do they ask chat GPT to do?
So they focused on quantum gates.
These are the like fundamental building blocks of quantum circuits.
First, they asked it to define different types of gates, you know, explain what they do.
Then they wanted it to create a visual representation of each gate, like a diagram.
And finally, the real challenge.
They give it specific quantum states and asked it to calculate the final state
after applying a certain gate.
Whoa, that's hardcore.
So did chat GPT's brain melt?
Well, we'll find out after a quick break.
Oh, come on.
You can't leave us hanging like that.
Oh, all right.
All right, no break.
Let's get right back into it.
So we left off with chat GPT facing the quantum challenge, defining those gates,
drawing them and calculating final states.
Yeah, I'm on the edge of my seat here.
Did it pass?
Well, it was kind of a mixed bag, honestly, like its performance on level two.
Oh, no.
When it came to defining the gates, it actually did OK.
Like it could explain in words what each gate does.
So it knows its quantum vocabulary.
Yeah, it seems like it can grasp the basic theory pretty well.
But what about the diagrams?
Could it draw those accurately?
Hmm, that's where things got a little messy.
It tried, but the diagrams were often incomplete or had errors.
So it's like explaining a dance move, getting the steps wrong.
Yeah, something like that.
It's like it's missing the visual language of quantum circuits.
OK, and what about that final challenge?
Calculating the final quantum states.
Ooh, that's where things really went south.
It got a lot of answers wrong.
And sometimes it would even contradict itself.
Like it was sure it was right even when it was wrong.
Yeah, pretty much.
Which is kind of scary, right?
Especially with something like quantum computing where precision is key.
Yeah, you don't want it to be confidently wrong.
So does this mean chat GPT just doesn't get quantum mechanics?
It seems that way.
Like it's just mimicking patterns from its training data,
not really understanding the underlying principles.
Hmm.
So it's like trying to solve a math problem without knowing the concepts.
Exactly.
You might get lucky sometimes, but mostly you'll be way off.
Did the researchers have any ideas about why it struggles with this?
They think it might be because chat GPT relies on statistics.
And quantum mechanics is all about probability.
Like the outcome isn't always certain.
Ah, so it's used to things being more black and white.
Yeah, and the quantum world is anything but.
So what's the lesson here?
Don't blindly trust the AI, especially with complex topics.
Always double check, use your own knowledge.
Right, that GPT can be a helpful tool, but it's not a magical oracle.
Definitely not.
The researcher said it's not ready to be the main source for learning quantum computing.
More like a study buddy that you got to keep an eye on.
OK, that makes sense.
But earlier you mentioned this idea of using chat GPT's flaws as a teaching tool,
like trying to trick it into making mistakes.
That sounds pretty interesting.
It is.
Instead of seeing those flaws as roadblocks, turn them into learning opportunities.
So like a game of catch the AI error.
Exactly.
Students would have to really think critically to do that.
And in the process they'd learn a lot about the topic itself.
Right.
And maybe even some strategies for evaluating AI in general.
So it's not just about computer science, it's about critical thinking skills for the future.
Exactly.
The researchers even suggest having students create their own data sets.
Data sets specifically designed to trip up chat GPT.
Whoa, that's taking it to a whole new level.
It would be like training AI auditors.
Students who can really assess the strengths and weaknesses of these tools.
That's a pretty amazing idea.
Yeah.
So instead of just using AI, they're learning to understand it from the inside out.
Yep.
And that could be a game changer for education.
OK, this is all really fascinating.
But before we get too carried away, did the researchers have any other practical tips
for using chat GPT in education?
They did.
They think it can be really valuable for collaborative learning.
Like a virtual study group member.
Yeah, exactly.
So instead of just getting answers from it, students use it to spark discussion.
Right.
It becomes part of the learning process, not a replacement for it.
I like that.
So they could ask it to generate some code, then analyze it together.
Yep.
Discuss what's good, what's bad, come up with their own solutions.
That's much more engaging than just working alone.
It is.
And it fits with the trend of more active learning in education.
Awesome.
So big takeaway.
Chat GPT can be a great tool, but use it strategically.
Absolutely.
Think about your goals and don't be afraid to experiment.
All right.
Well, we've covered a lot of ground here, from basic coding to quantum mechanics,
and now to teaching strategies.
It's a lot to think about.
It is.
But one thing's for sure.
Chat GPT is shaking things up in computer science education.
No doubt about it.
And it's raising some big questions about the future of learning in general.
Big questions, indeed.
OK.
Well, we're almost out of time for today.
But I'm really curious about this idea of Chat GPT democratizing access to education.
Could it make learning more accessible and engaging for everyone?
That's a great question.
And a whole other topic we could dive into.
Yeah, maybe next time.
But for now, I want to thank you for joining me on this deep dive.
It's been a pleasure, as always.
And to all our listeners, thanks for tuning in.
We'll see you next time on the deep dive.
So we've seen how Chat GPT can be both brilliant and baffling.
Acing basic coding, but struggling with those complex quantum calculations.
Yeah, a real mixed bag.
But beyond those specific tests, what about the bigger picture?
Could there be some unexpected benefits of using Chat GPT in computer science education?
Oh, absolutely.
Like, think about accessibility.
Chat GPT could be a huge help for students who don't have traditional resources.
Like, maybe they don't have a good tutor or a well-equipped computer lab.
Right, so it could kind of level the playing field.
Give everyone a chance to learn, regardless of their background.
Exactly.
And it's always available 24-7.
Yeah, like a coding buddy who never sleeps.
Uh-huh, pretty much.
And it could make learning more engaging, too.
OK, yeah, sometimes computer science can feel kind of dry, especially for beginners.
Right, but what if you could use Chat GPT to gamify the learning process?
Well, I like that.
Like, turn it into a challenge, see if you can outsmart the AI.
Or use it to create interactive coding puzzles.
Oh, it definitely make it more fun.
And it could even help personalize the learning experience.
Oh, so?
Well, Chat GPT could adapt to each student's pace.
Give them targeted feedback, suggest resources based on their interests.
So it's like a custom-made learning pass for every student.
Exactly.
Of course, we have to be careful about bias in the data and make sure it's used ethically.
Right, we don't want to create new problems while trying to solve old ones.
Definitely not.
But overall, it seems like Chat GPT could be a really positive force in computer science education.
I think so.
It's not a replacement for good teachers or human interaction.
Right, it's a tool, not a teacher.
But it's a tool with a lot of potential to make learning more accessible, engaging, and personalized.
So for our listeners who are maybe thinking about using Chat GPT in their own learning, what's your advice?
Don't be afraid to experiment.
Try different things.
See what works for you.
And most importantly, keep thinking critically.
Absolutely, because that's something no AI can teach you.
Well said.
It's been a fascinating deep dive, exploring all the possibilities and challenges of Chat GPT.
I agree, it's been a great discussion.
And to all our listeners, thank you for joining us on the deep dive.
Keep exploring, keep learning, and we'll see you next time.