Imagine a bodybuilder in training, let’s call him Ronnie, who is about to attempt an 800 lbs squat. This time, however, his trainer has brought a new tool: the Weightlifter. “With this Weightlifter, your 800 lbs squat will be a piece of cake, Ronnie!”. Indeed, Ronnie lifts the 800 lbs without feeling the annoying aches and pains in his joints, and his lungs feel full and strong… thanks to the Weightlifter. “You can lift heavier weights than you could ever imagine!”. Suddenly, breaking the squat world record doesn’t require years of continuous dedication and sacrifices. The Weightlifter is a wonderful tool! After briefly basking in his success, Ronnie wonders, “Does this tool really make me a better bodybuilder?”.
We face a similar situation in education with the introduction of Artificial Intelligence (AI) software, like Chat Generative Pre-Trained Transformer (ChatGPT). With such tools, students are able to generate (parts of) essays or answers to test questions within a fraction of a second. In a way, this kind of “help” is not new. One could ask a friend or a family member for assistance, or even pay some expert found on the internet. The difference is that the bar to get external support has dramatically been lowered. Where one would’ve previously had to find the right expert, agree on a (not insignificant) fee, and wait for the assignment to be delivered, one can now get all the answers immediately and relatively cheaply, even for free.
In the case of our bodybuilder, Ronnie, we understand that the Weightlifter does not make him an accomplished bodybuilder. He needs to lift the weights himself. His goal is not to lift the weight because it needs to be displaced (because it blocks a passage, for example); he needs to lift because he needs to improve his muscles. Similarly, students do not write an essay because the professor has a hobby of collecting essays; students write an essay because they need to become better writers and engage with the content of the lessons. A university is not a place to find the easiest route to submitting an assignment; rather, a university is a place for mindbuilders.
Building a body requires time, dedication, effort, perseverance, and challenges. If Ronnie isn’t sweating at the gym, something is wrong. What would this look like in the practice of building minds? The Swedish psychologist, Anders Ericsson, found a general principle of learning by investigating what the top performers in the fields of chess, music, and sports have done to become so successful. He found that they all apply the same form of practice, something Ericsson calls deliberate practice, which is, according to him, “the most effective and powerful form of practice that we know of” (Ericsson & Pool 2016, p. 53). Ericsson formulated a couple of conditions that yield deliberate practice, but I want to single out two features that particularly impact the students’ subjective learning experience:
- “Deliberate practice takes place outside one’s comfort zone and requires a student to constantly try things that are just beyond his or her current abilities. Thus it demands near-maximal effort, which is generally not enjoyable” (Ericsson & Pool 2016, p. 228).
- “Deliberate practice is deliberate, that is, it requires a person’s full attention and conscious actions” (Ericsson & Pool 2016, p. 229).
Of course, these principles for learning will be implemented differently depending on whether one studies math, biology, economics, or literature. But for students to become better mindbuilders, according to the best research we have, they need to struggle and be challenged, which is “generally not enjoyable”.
So if students at home can choose between an activity that is generally not enjoyable and a means to easily circumvent this activity with the same result, namely, to produce answers to an assignment, it seems obvious which choice students will make—especially when it is nearly impossible to detect with the current tools available to teachers. Any appeal to one’s conscience or ethical principles becomes practically futile, as long as one can be invisible while cheating. Some students may resist this temptation because they are intrinsically motivated or because they adhere to strong ethical principles, but I think it would not be too farfetched to expect an overall increase of such misuse.
Chess engines are now ubiquitous in professional chess. But chess players do not compete with their own chess engine against one another. Instead, chess engines are used in training situations: for example, one uses the engine to analyze a specific position. In order to gain any benefit, the player needs to have completed the not-so-enjoyable part of finding the best move. If the engine spits out the best move, the chess player can compare this solution with the chess engine’s. But if the chess player has not put in any effort beforehand, any solution generated by the engine would be useless to the player’s self-development.
Chess players are not evaluated by submitting solutions to their chess exercises to a chess university, but by actually playing chess against someone else. University students, on the other hand, are often evaluated by the same activity they need to do to become better: the training becomes the tournament. Maybe by disentangling the training from the tournament, like in chess and bodybuilding, we can find a way that the rise of readily available AI tools will not impair learning. We may disagree on how this can be accomplished, but we should agree that a university aims to develop and cultivate the minds of students, and not produce consumers of pre-trained chat bots.
Ericsson, A. & Pool, R. (2016). Peak: Secrets from the New Science of Expertise. Houghton Mifflin Harcourt: Boston, MA & New York, NY.