Dean of LLM Tutors: Exploring Comprehensive and Automated Evaluation of LLM-generated Educational Feedback via LLM Feedback Evaluators

• URL: http://arxiv.org/abs/2508.05952v1
• Date: Fri, 08 Aug 2025 02:36:23 GMT
• Title: Dean of LLM Tutors: Exploring Comprehensive and Automated Evaluation of LLM-generated Educational Feedback via LLM Feedback Evaluators
• Authors: Keyang Qian, Yixin Cheng, Rui Guan, Wei Dai, Flora Jin, Kaixun Yang, Sadia Nawaz, Zachari Swiecki, Guanliang Chen, Lixiang Yan, Dragan Gašević,
• Abstract summary: We propose a method that uses LLM feedback evaluators to automatically and comprehensively evaluate feedback generated by LLM tutors.<n>This allows low-quality feedback to be rejected and enables LLM tutors to improve the feedback they generated based on the evaluation results.<n>Our findings show that o3-pro demonstrated the best performance in zero-shot labelling of feedback while o4-mini demonstrated the best performance in few-shot labelling of feedback.
• Score: 5.838566576554449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The use of LLM tutors to provide automated educational feedback to students on student assignment submissions has received much attention in the AI in Education field. However, the stochastic nature and tendency for hallucinations in LLMs can undermine both quality of learning experience and adherence to ethical standards. To address this concern, we propose a method that uses LLM feedback evaluators (DeanLLMs) to automatically and comprehensively evaluate feedback generated by LLM tutor for submissions on university assignments before it is delivered to students. This allows low-quality feedback to be rejected and enables LLM tutors to improve the feedback they generated based on the evaluation results. We first proposed a comprehensive evaluation framework for LLM-generated educational feedback, comprising six dimensions for feedback content, seven for feedback effectiveness, and three for hallucination types. Next, we generated a virtual assignment submission dataset covering 85 university assignments from 43 computer science courses using eight commonly used commercial LLMs. We labelled and open-sourced the assignment dataset to support the fine-tuning and evaluation of LLM feedback evaluators. Our findings show that o3-pro demonstrated the best performance in zero-shot labelling of feedback while o4-mini demonstrated the best performance in few-shot labelling of feedback. Moreover, GPT-4.1 achieved human expert level performance after fine-tuning (Accuracy 79.8%, F1-score 79.4%; human average Accuracy 78.3%, F1-score 82.6%). Finally, we used our best-performance model to evaluate 2,000 assignment feedback instances generated by 10 common commercial LLMs, 200 each, to compare the quality of feedback generated by different LLMs. Our LLM feedback evaluator method advances our ability to automatically provide high-quality and reliable educational feedback to students.

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