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Investigating Learner-Aware Design of LLM-Generated Educational Feedback

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Computer Science > Computation and Language

arXiv:2602.11650 (cs)
[Submitted on 12 Feb 2026 (v1), last revised 12 Aug 2026 (this version, v2)]

Title:Investigating Learner-Aware Design of LLM-Generated Educational Feedback

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Abstract:Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and information coverage) to support answer revision and learner acceptance across diverse learner profiles. We define six feedback designs for multiple-choice biology questions, including a baseline design and variants with additional feedback elements, and conduct an empirical study with high school students. We evaluate feedback using immediate revision performance and six subjective evaluation criteria, and analyze how feedback preferences vary across learner profiles based on personality traits. Our results show that feedback with clear and comprehensive guidance improves revision performance and receives favorable evaluations across learner profiles, whereas informational novelty and affective framing vary across profiles. These findings suggest that learner profiles should be considered when designing LLM-generated feedback.
Comments: Under Review
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2602.11650 [cs.CL]
  (or arXiv:2602.11650v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2602.11650
arXiv-issued DOI via DataCite

Submission history

From: Momoka Furuhashi [view email]
[v1] Thu, 12 Feb 2026 07:02:33 UTC (968 KB)
[v2] Wed, 12 Aug 2026 16:48:33 UTC (1,121 KB)
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