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

arXiv: https://arxiv.org/abs/2609.35376 · HTML: https://mlsanity.com/paper/2609.35376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [Quantifying the Persona Effect in LLM Simulations](https://mlsanity.com/paper/2402.10811/) (2024)
- [PersonaGym: Evaluating Persona Agents and LLMs](https://mlsanity.com/paper/2407.18416/) (2024)
- [SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors](https://mlsanity.com/paper/2510.17516/) (2025)
- [Let Me Teach You: Pedagogical Foundations of Feedback for Language Models](https://mlsanity.com/paper/2307.00279/) (2023)
- [Automatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technology](https://mlsanity.com/paper/2411.03495/) (2024)
- [Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization](https://mlsanity.com/paper/2604.07343/) (2026)
- [A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities](https://mlsanity.com/paper/2604.11048/) (2026)
- [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)
- [Simulating Student-Tutor Interactions with LLMs for Multilingual Math Feedback](https://mlsanity.com/paper/2506.04920/) (2025)
- [LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop](https://mlsanity.com/paper/2507.04295/) (2025)
- [Dean of LLM Tutors: A Framework for Automated Quality Review of AI-generated Feedback](https://mlsanity.com/paper/2508.05952/) (2025)
- [Investigating Learner-Aware Design of LLM-Generated Educational Feedback](https://mlsanity.com/paper/2602.11650/) (2026)

## Abstract (arXiv)

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.