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October 16, 2025Open Access

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

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Authors

KQKeyang QianYCYixin ChengRGRui Guan

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Overview

Analysis shows automated llm feedback evaluators enhance educational feedback quality, suggesting improved learning outcomes for students.

Key Points

  • Automated evaluation of feedback enhances learning quality, addressing ethical concerns in llm usage.
  • LLM feedback evaluators identified 2,000 feedback instances, revealing performance differences among various llm models.
  • Using a comprehensive framework, the study assessed feedback content and effectiveness across multiple dimensions.
  • GPT-4.1 achieved human expert-level performance, indicating the potential of llm feedback evaluators in educational settings.

Cite This Study

Qian et al. (2025) studied this question.

synapsesocial.com/papers/68f0f51d8dd8ea469b1d71bfhttps://doi.org/10.48550/arxiv.2508.05952
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1LLM-generated formative feedback in education: A qualitative systematic literature review2026 · 3 citations
  2. 2LLM-Generated Feedback in L2 Writing: A Scoping Review2026
  3. 3Using Large Language Models to Augment (Rather Than Replace) Human Feedback in Higher Education Improves Perceived Feedback Quality2024 · 2 citations
  4. 4Boundary Conditions for LLM-Generated Feedback in Primary Writing: An Educator-Aligned Evaluation and Design Considerations2026
  5. 5Evaluating the Impact of Advanced LLM Techniques on AI-Lecture Tutors for a Robotics Course2024