How Well Can LLMs Simulate Real Learner Evaluations of Educational Feedback?

Title:How Well Can LLMs Simulate Real Learner Evaluations of Educational Feedback?

Abstract:While recent studies have explored human behavior and preference simulation using large language models (LLMs), it remains unclear how well LLMs can simulate subjective evaluations from real learners in educational settings. We investigate this question using real learner evaluation data on feedback for high-school biology questions at both the group and individual levels. We compare performance with and without learner-specific information, such as personality traits and evaluation examples, across six models. Our results show that LLMs still have a limited ability to simulate learner evaluations. Providing learner profiles and examples improves score calibration and individual-level simulation, but more often fails to improve group-level consistency. These findings highlight the need to investigate which learner information and adaptation strategies are effective for learner preference simulation.

Computation and Language (cs.CL)

arXiv:2609.35376 [cs.CL]

(or arXiv:2609.35376v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.35376 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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