# Investigating Learner-Aware Design of LLM-Generated Educational Feedback

arXiv: https://arxiv.org/abs/2602.11650 · HTML: https://mlsanity.com/paper/2602.11650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors](https://mlsanity.com/paper/2510.17516/) (2025)
- [Enhancing LLM-Based Feedback: Insights from Intelligent Tutoring Systems and the Learning Sciences](https://mlsanity.com/paper/2405.04645/) (2024)
- [Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents](https://mlsanity.com/paper/2502.13012/) (2025)
- [Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System](https://mlsanity.com/paper/2005.02431/) (2020)
- [Let Me Teach You: Pedagogical Foundations of Feedback for Language Models](https://mlsanity.com/paper/2307.00279/) (2023)
- [Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style](https://mlsanity.com/paper/2505.19173/) (2025)
- [FeedEval: Pedagogically Aligned Evaluation of LLM-Generated Essay Feedback](https://mlsanity.com/paper/2601.04574/) (2026)
- [The Revolution Has Arrived: What the Current State of Large Language Models in Education Implies for the Future](https://mlsanity.com/paper/2507.02180/) (2025)
- [Dean of LLM Tutors: A Framework for Automated Quality Review of AI-generated Feedback](https://mlsanity.com/paper/2508.05952/) (2025)

## Built on by (most cited first, 1 of 1)

- [How Well Can LLMs Simulate Real Learner Evaluations of Educational Feedback?](https://mlsanity.com/paper/2609.35376/) (2026)

## Abstract (arXiv)

Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and coverage) to support answer revision and learner evaluations across learner profiles. We define six feedback designs for multiple-choice biology questions, including a baseline design and five variants with additional feedback elements, and conduct an empirical study with 321 high school students. We evaluate feedback using immediate revision performance and six subjective evaluation criteria, and analyze differences in subjective evaluations across learner profiles based on personality traits. Our results show that presenting task-relevant information clearly is associated with better immediate revision performance and is favorably evaluated across learner profiles, while we observe descriptive differences in evaluation patterns, particularly for informational novelty and affective framing. These findings support further investigation of personalized LLM feedback design.