Fabulous and timely. Very interesting references. I used the book The Art of Computer Programming by Davidson and colleagues to try to understand computational thinking .
I just finished a two-semester Python course. I did pretty well, and I liked my teachers. But the whole time, it bothered me that SO much energy was being expended on preventing me from using the tools I would undoubtedly be using from Day One if I were to get a job in the field (or to use my newly attained knowledge as I intend to.) It’s like teaching someone to be a carpenter while banning use of a nail gun and electric screwdriver. I would prefer the capenter working on my house to have great proficiency with both.
I have been spending too much time memorizing exception types to think much about the question of how this could be done better. But I think the author's suggestions here are excellent. Suppose I had seen a longish question on my exam that said "Here are four code blocks approaching the problem of _________. Evaluate the efficacy of each, under various circumstances." My eyes would have glazed over, while I'm thinking "I'm going to have to strain my brain over this." Isn't that what employers want their new hires to be able to do?
Phasing all this in will be tricky. But I do agree that institutions that fail to get ahead of this curve are doing no one any good, least of all themselves.
Exactly, Luis. Thank you for instantiating what I wrote with a very recent experience of yours. It will be tricky indeed, but I have confidence in smart folks coming together and making some hard decisions.
This is the fundamental issue to address in education, so thank you Amarda. @luisgranadosdc also very well expressed in your comment. I am a business school professor and programme designer and the debate I am having with my colleagues- especially those who are still active professionals and especially those who are younger and in tech native businesses: how do we teach in a way that students learn the necessary substrate knowledge and skills without making people use obsolete methods and tools.
My experience so far is that there are no easy answers. LLMs - used in almost any way in education - are more likely to damage fundamental learning than to enhance it, or even to allow real learning to occur unless we are teaching how to use LLMs to do things for you. But without the fundamental learning / substrate, learners have no criteria to correctly plan or judge the outputs.
I am beginning to meet people at Master's programme level who became extremely over-dependent on LLMs during their undergraduate studies. It is not that they have no substrate in particular areas of skills or knowledge, they have no substrate in their thinking. Unable to formulate appropriate questions to ask an LLM, for example. Unable to interpret insightful questions they are asked to answer. Unable to see glaring incoherence in their reasoning from one minute to the next.
GenAI is 99.9% (gotta have some hope) going to remain a part of our present and future even if it the majority of its use is low value, as now.
So thanks again for stating the issue clearly and offering potential approaches to resolving it. If we don't, we will very soon have generations arriving at adulthood who are largely dependent on these tools - not because they are lazy, but because they are incapable of thinking properly for themselves.
Thank you for sharing your experience, Antony. The redesign is difficult and will require folks that are both clear-eyed and courageous. It will be bumpy.
Bumpy indeed. I'll keep that, a nicer way of saying it that I have been. Best of luck with your work at George Mason. From the outside it looks like a very interesting role they have created and you have taken on. So much better to face GenAI/AI and its consequences head-on and transparently.
Fabulous and timely. Very interesting references. I used the book The Art of Computer Programming by Davidson and colleagues to try to understand computational thinking .
I just finished a two-semester Python course. I did pretty well, and I liked my teachers. But the whole time, it bothered me that SO much energy was being expended on preventing me from using the tools I would undoubtedly be using from Day One if I were to get a job in the field (or to use my newly attained knowledge as I intend to.) It’s like teaching someone to be a carpenter while banning use of a nail gun and electric screwdriver. I would prefer the capenter working on my house to have great proficiency with both.
I have been spending too much time memorizing exception types to think much about the question of how this could be done better. But I think the author's suggestions here are excellent. Suppose I had seen a longish question on my exam that said "Here are four code blocks approaching the problem of _________. Evaluate the efficacy of each, under various circumstances." My eyes would have glazed over, while I'm thinking "I'm going to have to strain my brain over this." Isn't that what employers want their new hires to be able to do?
Phasing all this in will be tricky. But I do agree that institutions that fail to get ahead of this curve are doing no one any good, least of all themselves.
Exactly, Luis. Thank you for instantiating what I wrote with a very recent experience of yours. It will be tricky indeed, but I have confidence in smart folks coming together and making some hard decisions.
This is the fundamental issue to address in education, so thank you Amarda. @luisgranadosdc also very well expressed in your comment. I am a business school professor and programme designer and the debate I am having with my colleagues- especially those who are still active professionals and especially those who are younger and in tech native businesses: how do we teach in a way that students learn the necessary substrate knowledge and skills without making people use obsolete methods and tools.
My experience so far is that there are no easy answers. LLMs - used in almost any way in education - are more likely to damage fundamental learning than to enhance it, or even to allow real learning to occur unless we are teaching how to use LLMs to do things for you. But without the fundamental learning / substrate, learners have no criteria to correctly plan or judge the outputs.
I am beginning to meet people at Master's programme level who became extremely over-dependent on LLMs during their undergraduate studies. It is not that they have no substrate in particular areas of skills or knowledge, they have no substrate in their thinking. Unable to formulate appropriate questions to ask an LLM, for example. Unable to interpret insightful questions they are asked to answer. Unable to see glaring incoherence in their reasoning from one minute to the next.
GenAI is 99.9% (gotta have some hope) going to remain a part of our present and future even if it the majority of its use is low value, as now.
So thanks again for stating the issue clearly and offering potential approaches to resolving it. If we don't, we will very soon have generations arriving at adulthood who are largely dependent on these tools - not because they are lazy, but because they are incapable of thinking properly for themselves.
Thank you for sharing your experience, Antony. The redesign is difficult and will require folks that are both clear-eyed and courageous. It will be bumpy.
Bumpy indeed. I'll keep that, a nicer way of saying it that I have been. Best of luck with your work at George Mason. From the outside it looks like a very interesting role they have created and you have taken on. So much better to face GenAI/AI and its consequences head-on and transparently.