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At the end of the semester, many of us talking about AI and assessment. I’m in my second year attempting in-class exams where students are allowed to use LLMs. x.com/lukestein/status/1920283805333545267 Quick example of an exam question that’s been interesting to see. (1/
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GPT 4o confidently asserts there are four such companies. If pressed, it will admit that there are maybe five. Or six. Or seven. If we tell it all ten, it will apologize for the oversight. chatgpt.com/share/681bf97b-3490-8002-ae30-c110724c9d4c
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Claude 3.7 Sonnet finds 9. It is more circumspect ("reflects my knowledge as of October 2024, and there may have been changes to the S&P 500 composition since then.") claude.ai/share/926f4d42-aced-4d4f-8b36-08768c7d40ca
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Perplexity says seven, but if pressed agrees maybe it's nine. No wait, not nine; it's definitely seven. perplexity.ai/search/how-many-single-letter-ticker-4gILUkTERDaoWbMG93sQlw
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(bw, chatGPT o3 nails it with three minutes of thinking. That model was released publicly two weeks after this midterm exam.) chatgpt.com/share/681cb11c-6f08-8002-b3c3-c13909174b2d
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Of course, it's more interesting to think about whether/why there might be a relationship between ticker length and tariff announcement effects. There are many potential interesting mechanisms. If students think about some—even if an LLM proposed them—great!
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Plausible hypotheses go in different directions! And in talking about how they might assess mechanisms, strong student answers made reference to specific data (e.g., around international trade, corporate supply chains, liquidity) and analytical techniques we discussed in class.
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