Do Large Language Models Mentalize When They Teach?
How do LLMs decide what to teach next: by reasoning about a learner's knowledge, or by using simpler rules of thumb? We test this in a controlled task previously used to study human teaching strategies. On each trial, a teacher LLM sees a hypothetical learner's trajectory through a reward-annotated directed graph and must reveal a single edge so the learner would choose a better path if they replanned. We run a range of LLMs as simulated teachers and fit their trial-by-trial choices with the same cognitive models used for humans: a Bayes-Optimal teacher that infers which transitions the learner is missing (inverse planning), weaker Bayesian variants, heuristic baselines (e.g., reward base...
| Authors | Sevan K. Harootonian, Mark K. Ho, Thomas L. Griffiths et al. |
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| Primary category | cs.AI |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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