>
> I’m curious if others have tried similar methods or have insights on
> striking the right balance between leveraging AI tools and preserving
> hands-on learning opportunities.


Hi Federica,

If you hadn't seen it already, there was this thread that Lex started in
the slack last year on this topic - I think there are quite a few links to
examples
https://carpentries.slack.com/archives/C03LE48AY/p1713166328175869

Best,
--
Hao Ye
(he/him/his)
[email protected]


On Tue, Mar 18, 2025 at 12:13 PM Federica Gazzelloni <
[email protected]> wrote:

> Hi all,
>
> This thread continues to highlight such important insights. I wanted to
> add another perspective, building on what’s been said.
>
> A key distinction I often emphasize in my own teaching is the relationship
> between effort and comprehension. It’s true that AI tools can make the
> process of problem-solving more efficient, but it’s also critical to ensure
> that learners are actively engaging with the concepts, rather than
> passively accepting solutions.
>
> One possible approach is introducing structured exercises that leverage AI
> as a learning companion. For instance:
>
> Students can use AI tools to draft initial code.
>
> They then analyze and critique the AI's output—identifying potential
> errors, inefficiencies, or areas for improvement.
>
> Finally, they optimize the code manually, applying their own knowledge to
> refine and debug.
>
> This strategy shifts the focus from simply generating output to
> understanding the "why" and "how" behind it. It also aligns with the
> growing need to teach "meta-skills" for interacting with AI: evaluating its
> reliability, fine-tuning its outputs, and balancing automation with human
> creativity.
>
> Hao's analogy with meal kits is particularly apt here—AI can provide the
> "ingredients" or the base recipe, but students still need to understand how
> to "cook," adapting the process to suit their specific needs or goals. This
> fosters a deeper understanding of the coding principles and encourages
> critical thinking.
>
> I’m curious if others have tried similar methods or have insights on
> striking the right balance between leveraging AI tools and preserving
> hands-on learning opportunities.
>
> Best,
> Federica
>
>
> On Tue, Mar 18, 2025 at 4:58 PM Hao Ye <[email protected]> wrote:
>
>> If we only care about the end result (e.g. a function that computes
>>> something) and not the means to the end (e.g. how you compute something),
>>> it may be fine to teach an AI heavy methodology. We, as teachers, first
>>> have to decide what we want a student to learn before deciding the means to
>>> get them to that objective. We still teach mathematics to students even
>>> though we've been able to answer most all math questions through the
>>> college level with Wolfram Alpha for 15 years now and many things can be
>>> done on a calculator for 50 years now.
>>
>>
>> I am strongly in agreement with the distinction here between learning
>> programming vs. learning *some* set of skills whose end product is a
>> correct program.
>>
>> And because I like my cooking analogies, I'll add in an additional
>> metaphor of learning to cook vs. following the instructions in a meal kit.
>>
>> I am also reminded about the pedagogical literature findings that people
>> are bad at evaluating how much they've learned; and one of the common
>> estimators people use (that is biased) is how *easy* the learning was
>> (e.g. passively listening to a lecture vs. active learning).
>>
>> Best,
>> --
>> Hao Ye
>> (he/him/his)
>> [email protected]
>>
>>
>> On Tue, Mar 18, 2025 at 7:31 AM Jason Moore <[email protected]> wrote:
>>
>>> I think you have to carefully evaluate what the learning objectives are
>>> before deciding on AI or not or how much AI, etc. Take the analogy of
>>> learning an ancient way of making bowls by hand with wood versus learning
>>> how operate a manufacturing plant's bowl making machine. For the latter,
>>> you may not even need to know what a carving knife is or how the blade
>>> interacts with wood. In computer science, we have similar analogies tied to
>>> learning to code in assembly versus python. I can engineer a solution with
>>> either language and get something that does the same thing but I need
>>> different knowledge and understanding to succeed with each language. Of
>>> course we can teach students "how to code" using AI but they learn
>>> different things and the focus may mostly be on the product. If we only
>>> care about the end result (e.g. a function that computes something) and not
>>> the means to the end (e.g. how you compute something), it may be fine to
>>> teach an AI heavy methodology. We, as teachers, first have to decide what
>>> we want a student to learn before deciding the means to get them to that
>>> objective. We still teach mathematics to students even though we've been
>>> able to answer most all math questions through the college level with
>>> Wolfram Alpha for 15 years now and many things can be done on a calculator
>>> for 50 years now. If we want a student to know how every line in a program
>>> works, then they probably need to think about and write every line of that
>>> code. I listened to a recent podcast, the name is escaping me, where the
>>> caster lamented on students using AI to get the product, a written essay,
>>> but not learning how to write. Do we really want to stop teaching people
>>> how to write because a computer can write things?
>>>
>>> Jason
>>> moorepants.info
>>> +01 530-601-9791
>>>
>>>
>>> On Tue, Mar 18, 2025 at 11:25 AM Paola Di Maio <[email protected]>
>>> wrote:
>>>
>>>> Simon, and Toby whose sentiment is echoed-
>>>>
>>>> thanks for the discussion points
>>>>
>>>> In my personal experience, it is important to use the new tools
>>>> responsibly and intelligently.
>>>> if they can help us learn faster and better. Because there is too much
>>>> to learn, esp for younger generations
>>>> There is a race going on, and machines can give us some edge
>>>>
>>>> I feel that my mission is how to teach how to use AI responsibly* and
>>>> critically*
>>>>
>>>> which in fact is true of any technology, starting from evaluating
>>>> critically search results of any query
>>>>
>>>> So rather than saying do not use AI code generators, I d say
>>>>
>>>> a) continue to learn how to code, but use that knowledge
>>>> to write good prompts for the code generators and
>>>> b) learn how to evaluate debug, proof, test, implement, integrate,
>>>> test. evaluate, optimise  code
>>>> *shameless self promotion: I teach a course on responsible use of AI if
>>>> anyone is interested/able to collaborate with me on offering it
>>>>
>>>> By all means we must continue to learn and teach how to code - bur we
>>>> do not have to do the work ourselves
>>>> we become supervisors, system integrators etc
>>>>
>>>> How to use code generators to produce the code we want/need and how to
>>>> evaluate the qualify of ai generated code?
>>>> how to evaluate the output of any technology mediated process,really,
>>>> is what I think we help learners with
>>>>
>>>> I attach three relevant short posts in PDF from my feed
>>>> *disclaimer A*I supported, let me know if you spot terrible wrongs*
>>>>
>>>> for general interest/discussion and it would be great to connect over
>>>> our shared experiences
>>>> of using AI tor teaching/learning
>>>>
>>>> PDM
>>>>
>>>> On Mon, Mar 17, 2025 at 5:26 PM Waldman, Simon via discuss <
>>>> [email protected]> wrote:
>>>>
>>>>> I think the reasoning behind the “don’t use AI when you’re learning”
>>>>> comment is that there is a risk that people simply use code that they are
>>>>> given without thinking about it, and therefore don’t build a mental model
>>>>> of what is going on, don’t learn, and hence are unable to spot mistakes by
>>>>> the AI or build more advanced things.
>>>>>
>>>>>
>>>>>
>>>>> In a sense, I think it’s best to think of AI coding assistance as a
>>>>> more advanced version of “look it up on StackOverflow”. We have all come
>>>>> across plenty of people who have done all their coding by copy-pasting
>>>>> snippets from StackOverflow without fully understanding them – as well as
>>>>> people who have laid the groundwork by learning what they are doing first,
>>>>> and **then** looked things up to much better effect. I think this is
>>>>> especially important for a first language – when one is learning
>>>>> programming as well as a particular syntax.
>>>>>
>>>>>
>>>>>
>>>>> That’s my 2p; clearly “don’t use AI” is not enforceable and won’t be
>>>>> adhered to by many, but it’s important that we explain the reason that 
>>>>> that
>>>>> advice is given (and maybe consider moderating it to “limit use of AI” or
>>>>> similar) so that hopefully most learners will bear it in mind.
>>>>>
>>>>>
>>>>>
>>>>> --
>>>>>
>>>>> Dr Simon Waldman / [email protected]
>>>>>
>>>>> Assistant Professor of Energy Technologies, Heriot-Watt University
>>>>>
>>>>> Programme lead for MSc Renewable & Sustainable Energy Transition
>>>>>
>>>>>
>>>>>
>>>>> *From:* Paola Di Maio <[email protected]>
>>>>> *Sent:* 16 March 2025 22:46
>>>>> *To:* discuss <[email protected]>
>>>>> *Subject:* Re: [cp-discuss] Re: Feedback Request: Lesson Updates on
>>>>> Generative AI
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> ******************************************************************
>>>>> *Caution: This email originated from a sender outside Heriot-Watt
>>>>> University. Do not follow links or open attachments if you doubt the
>>>>> authenticity of the sender or the content. *
>>>>> * *****************************************************************
>>>>>
>>>>>
>>>>>
>>>>> Thank you Toby and all for the discussion
>>>>>
>>>>> it is an important one, at many levels
>>>>>
>>>>>
>>>>>
>>>>> I have always detested coding, comparing it to unnecessary
>>>>> bricklaying  vs me being interested in information architectures
>>>>>
>>>>>
>>>>>
>>>>> When AI generated code became available I felt relief, the finally
>>>>> humanity has found a way of avoiding coding by hand
>>>>>
>>>>>
>>>>>
>>>>> My question is: is the AI generated code as good as, better or worse
>>>>> than humanly written code?
>>>>>
>>>>> Having the code written up already means learners must learn how to
>>>>> implement it and run it correctly
>>>>>
>>>>>
>>>>>
>>>>> can it be used to learn/teach about coding more productively - ie
>>>>> engaging learners to Implement debug, test, maniupate, evaluate the ai
>>>>> generated output
>>>>>
>>>>> and how to correct it and improve it, rather than putting their effort
>>>>> into writing
>>>>>
>>>>>
>>>>>
>>>>>  AI generated code could allow learners to move straight into the next
>>>>> level of coding, that is implementation
>>>>>
>>>>> Thorough understanding of how the syntax and logic of the program
>>>>> should still be required, but the human intelligence
>>>>>
>>>>> so rare and precious can be spared the tedious task of actually
>>>>> writing it
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Mon, Mar 17, 2025 at 6:21 AM Paul Harrison via discuss <
>>>>> [email protected]> wrote:
>>>>>
>>>>> Hi Toby,
>>>>>
>>>>> I'm following this ongoing discussion with interest. Great to see this
>>>>> being added to Carpentries material.
>>>>>
>>>>> We recommend that you avoid getting help from generative AI while you
>>>>> learn to code
>>>>>
>>>>>
>>>>>
>>>>> I was a bit surprised by this negative conclusion. My feeling would be
>>>>> that it isn't reasonable to expect people not to use these tools while
>>>>> learning, and therefore they need to know how to use them safely. And they
>>>>> do seem quite good at explaining code or suggesting different approaches.
>>>>>
>>>>>
>>>>>
>>>>> Here's a slide I used in a recent workshop, although I'm far from 100%
>>>>> happy with it.
>>>>>
>>>>>
>>>>> https://monashdatafluency.github.io/r-progtidy/slides/introduction.html#11
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> ------------------------------
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