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ARXIV:2604.01594 · LLM TEACHING & COGNITION · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.01594LLM TEACHING & COGNITIONSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALESevan K. Harootonian · Mark K. Ho · Thomas L. Griffiths · Yael Niv · Ilia Sucholutsky · arXiv
This research investigates whether Large Language Models exhibit mentalizing behavior when teaching, using cognitive models to analyze their decision-making processes in a simulated learning environment.
Opportunity summary
Pain This research investigates whether Large Language Models exhibit mentalizing behavior when teaching, using cognitive models to analyze their decision-making processes in a simulated learning environment.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
This research investigates whether Large Language Models exhibit mentalizing behavior when teaching, using cognitive models to analyze their decision-making processes in a simulated learning environment. We test this in a controlled task previously used…
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…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. In a baseline experiment matched to the stimuli presented to human subjects, most LLMs perform well, show little change in strategy over trials, and…
LLM Teaching & Cognition moved forward this cycle; last verified April 2026. Public score 3.0/10.
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This research investigates whether Large Language Models exhibit mentalizing behavior when teaching, using cognitive models to analyze their decision-making processes in a simulated learning environment.
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10.48550/arXiv.2604.01594This research investigates whether Large Language Models exhibit mentalizing behavior when teaching, using cognitive models to analyze their decision-making processes in a simulated learning environment.
Abstract
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 based), and non-mentalizing utility models. In a baseline experiment matched to the stimuli presented to human subjects, most LLMs perform well, show little change in strategy over trials, and their graph-by-graph performance is similar to that of humans. Model comparison (BIC) shows that Bayes-Optimal teaching best explains most models' choices. When given a scaffolding intervention, models follow auxiliary inference- or reward-focused prompts, but these scaffolds do not reliably improve later teaching on heuristic-incongruent test graphs and can sometimes reduce performance. Overall, cognitive model fits provide insight into LLM tutoring policies and show that prompt compliance does not guarantee better teaching decisions.
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PROBLEM
This research investigates whether Large Language Models exhibit mentalizing behavior when teaching, using cognitive models to analyze their decision-making processes in a simulated learning environment. We test this in a controlled task previously used to study human teaching s...
METHOD
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.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. In a baseline experiment matched to the stimuli presented to human subjects, most LLMs perform well, show little change in strategy over trials, and their graph-by-graph performance is similar to that of...
WHY NOW
LLM Teaching & Cognition moved forward this cycle; last verified April 2026. Public score 3.0/10.
prompt compliance does not guarantee better teaching decisions
Explicitly stated as conclusion in abstract
partial
most LLMs perform well, show little change in strategy over trials, and their graph-by-graph performance is similar to that of humans
Directly stated in abstract with clear comparison to human performance
partial
Model comparison (BIC) shows that Bayes-Optimal teaching best explains most models' choices
Explicitly stated in abstract with specific methodology (BIC)
partial
show little change in strategy over trials
Directly stated in abstract as an observed result
partial
these scaffolds do not reliably improve later teaching on heuristic-incongruent test graphs
Directly stated in abstract with specific condition (heuristic-incongruent test graphs)
partial
can sometimes reduce performance
Directly stated in abstract but qualified with 'can sometimes'
partial
models follow auxiliary inference- or reward-focused prompts
Directly stated in abstract as observed behavior
partial
cognitive model fits provide insight into LLM tutoring policies
Directly stated as conclusion in abstract
partial
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This research investigates whether Large Language Models exhibit mentalizing behavior when teaching, using cognitive models to analyze their decision-making processes in a simulated learning environment.
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LLM Teaching & Cognition
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