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Right after giving a short talk at the International Business Pedagogy Workshop on how best to use large language models for learning and discussing the topic with a great panel, I took a short break and never got a chance to revisit and reflect on our discussion.<\/p>\n
In this talk, I originally focused on asking a few questions, but we did not have much time during the panel to discuss the answers to the questions. I’ve revisited my slides from that panel and added a slide with some answers (last slide). You can find the updated deck for “Mind the AI Gap: Understanding vs. Knowing” at https:\/\/ozer.gt\/talks\/mind-the-ai-gap.html<\/p>\n
Bottom line:<\/p>\n
To promote understanding, it’s better to treat LLMs as knowledge aggregators<\/strong> (not oracles with answers<\/em>). This is what tools like Perplexity aim to do. Instead of asking simple and straightforward questions and expecting a<\/strong> correct answer, deconstructing questions (offline) before interacting with LLMs is likely to trigger reasoning and facilitate understanding (vs. just knowing).<\/p>\n In the classroom, this can be accomplished by encouraging students to think and reflect on the question offline before going online and interacting with LLMs. For example, a homework assignment can begin in the classroom with a task to deconstruct the problem before students go online to find a solution to the problem later.<\/p>\n