
1 minute read
A ChatGPT-agnostic approach
Before teaching a large undergraduate course in Computer Science theory, my co-instructor Dr. Pedro Paredes and I played around with ChatGPT to get a sense of how students might use it. We were most concerned about ChatGPT solving problem set questions from scratch, so we gave that a shot first. Every time Dr. Paredes or I queried ChatGPT and skimmed its response, I broke into a huge panic: “*&!%, it actually solved it, what are we going to do!?” (And what’s my role in society now!?) Yet when I read the responses a second time, I realized the solution was actually nonsense. ChatGPT seems to be phenomenal at producing answers that match the language structure of correct solutions (e.g. it makes good use of “Therefore,” “To see this, observe,” and “pigeonhole principle”), but the logical content is largely nonsense (e.g. it claims two is an irrational number, and 10 plus 10 is 10). Of course, detecting language structure is easy while skimming, but evaluating the underlying logic takes active thought.
Fortunately, we couldn’t find a way for ChatGPT to undermine the pedagogy of the course (I again initially panicked when we first queried ChatGPT after its “improved math capabilities,” but fortunately the answers are still nonsense). So, we ultimately decided to have a ChatGPT-agnostic policy. We put in effort explaining to students that ChatGPT solutions will be frustrating for graders to evaluate, and they’d ultimately receive lowerthan-blank scores (see here and here — we also tried to share thoughts on other potential uses). Of course, large language models may get better at logic in the future, and we’ll have to adapt.
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On a positive note, dissecting ChatGPT-generated solutions helps us teach the valuable skill of distinguishing between logically sound text and text that initially seems convincing but is ultimately BS — we’ve lightly incorporated this into the curriculum.
Matt Weinberg is an assistant professor in computer science. He can be reached by email at smweinberg@princeton.edu.