As an experienced computer science educator, I find myself grappling with multifaceted challenges, including students lacking effective learning methods, the difficulty in crafting exercises that strike the right difficulty balance, and the dwindling motivation of students to acquire new skills. Moreover, grading has become a puzzle in an environment where students may not be sufficiently challenged, leading to a diminished investment in their education.

Main Question:

  • How can computer science educators holistically address the interconnected challenges of designing effective exercises, motivating students to invest in their learning, and implementing grading strategies that reflect true skill acquisition, especially in the context of the rising influence of AI and the impact of the COVID-19 pandemic on students?


Exercise Design and AI Influence:

It’s been hard to offer students some exercises to help them learn the basis. They won’t really work on them and directly use AI tools, and then once I’d ask them to solve more complex exercises they will struggle and fail.

  • How can educators design exercises that challenge students while considering the prevalence of AI tools, ensuring that the learning process remains hands-on and engaging?
  • Are there strategies for crafting exercises that encourage foundational understanding without being overly simplistic or easily solved with AI assistance?

Motivation and Learning Environment:

  • In an era marked by reduced student motivation, compounded by the effects of COVID-19, what approaches can be adopted to create an engaging learning environment that fosters intrinsic motivation and active participation?
  • How can real-world applications and collaborative learning be integrated to revive students' enthusiasm for acquiring new skills?

Grading Strategies and Student Investment:

  • Given the challenge of grading in an environment where students may lack motivation and skills, what grading methods can effectively assess skill acquisition and incentivize continuous learning and improvement?
  • Are there grading systems that explicitly link grades to students' effort, growth, and overall investment in the learning process?

PS: I'm not anti AI tools, as I'm using them a lot as what they are designed to be : tools. However, students usually use them to replace their learning and replace them actually doing the work, that leads to a very poor understanding of thing. In the long run they will fail to grasp the real challenge of CS jobs in the future.

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    $\begingroup$ What evidence do you have for "reduced motivation"? $\endgroup$
    – Buffy
    Commented Feb 28 at 14:08
  • $\begingroup$ Note that just recently, the Nvidia CEO suggested kids NOT learn to code. AI is likely to take over low level coding tasks. developers.slashdot.org/story/24/02/26/1322242/… $\endgroup$
    – Buffy
    Commented Feb 28 at 14:14
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    $\begingroup$ @Buffy well in a class of 15 at the end I've had 4 students actually working on the project, asking questions and so on, while the rest of them were either sleeping or doing other stuff. Those students were on their last year of a top French engineering school. I've also been teaching to younger students too, one of them asked me a question, and after 20secs of answer, grabbed his phone and scrolled tik tok... I'm not the kind of "police" teacher, they are supposed to be adults and I'm only here to pass knowledge... What I wish for is to find new ways of teaching that would brought them in. $\endgroup$
    – YCN-
    Commented Feb 28 at 15:35
  • $\begingroup$ Well I'd like to believe that coding has become obsolete, but that's definitively not the case today. We engineers/developers are probably going to have a shift in our work that's for sure, but I think we would always be needed to take a project from idea to reality. $\endgroup$
    – YCN-
    Commented Feb 28 at 15:38
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    $\begingroup$ This is certainly worthwhile and interesting, but a bit broad. Just navigating AI grading is (at least) a thread of its own. It'd probably take a book or three to really answer this. Also, I don't think questions like this have one-size-fits-all solutions, so having some context for your particular situation might help ensure answers speak to your needs. $\endgroup$
    – ggorlen
    Commented Mar 2 at 4:48

1 Answer 1


Personal preference: ask folk to turn-off personal devices (they are a distraction to others, not just user) and if there's something impressively urgent, take it to the other side of the door - a better location! I'm also old-fashioned in recommending the use of log-/lab-books, both for lecture-notes and for jotting ideas and recording decisions/progress during dev.work, eg why did I decide to 'go that way'. NB course admin-stuff includes research papers supporting 'paper over screen'.

The GAI genie is out of the bottle. The only way to stuff it back in would be to ban Internet connections! That said, such tools offer the lazy student (who cannot see longer-term beyond his/her current assignment) a 'cognitive by-pass' short-cut. Hence, your question! Nevertheless, don't forget that many will go on to use such tools to assist them in the work-place. So, if a professional tool, shouldn't we be teaching best use of same, not banning it?

Accordingly, we have to adapt. Perhaps alter practical-exercises to include a viva-voce component? Trainees return with their completed work. Ask for outline of solutions, eg some use for-loop, others while. Taking one group at a time, request show code. Anyone different? Ask another (in group) for walk-through - someone not understanding will fail at this point. Look at the names - ChatGPT often uses x, y, z (like) choices in cookie-cutter solutions, rather than descriptive names (isn't naming one of the most difficult tasks in coding?). Exposed? Grade goes down! Next ask A.N.Other who claims same solution, and ask for demonstration of a minor change - again, cannot be achieved if do not understand original work. eg original assignment: print all digits from zero to nine, and next step becomes: only odd numbers (thus Beginners will add a 'guard' if-statement, but could require more advanced to substitute a generator within the for-control (am talking Python here)).

GitHub Copilot docs acknowledge this: “You are responsible for ensuring the security and quality of your code. We recommend you take the same precautions when using code generated by GitHub Copilot that you would when using any code you didn’t write yourself. These precautions include rigorous testing, IP scanning, and tracking for security vulnerabilities.” Need we say more?

As mentioned, if 'cheat' on early work, unable to complete later - particularly if build on earlier submissions. Thus, warn of grading system - unlikely that first, trivial, assignments are weighted equally with more advanced work. So, is it worth any anxiety? Similarly, if claims of 'faster development' are to be believed, shorten assignment dates, and leave more time for later work which will better expose who-knows-what.

Issues described are disconnect between intrinsic and extrinsic motivation. At the level of a trade/professional course, the former should be the driving force. We encourage, shape, direct, mentor, etc. Again, what is work-place scenario? Will 'boss' be interested in 'baby-ing' new hires?

Agree with what saying. Anyone can help an A+ student achieve a few more percent after this course. The person who can take a D-student and help him/her to a B result, that's an achievement. May I refer you to an old joke: How many psychologists does it take to change a light-bulb? None - but the light-bulb has to want to change!

Without more detail of the specific course(s) it might be difficult to offer help beyond such generalisations. However, happy to discuss and hope to learn from each-other...


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