Shuyi Fan, Boyuan Deng, Mengyu Xu, Jiale Liu, Hongyang Zhang (2026) — arXiv:2607.28128 (cs.CL, cs.AI, cs.CY)
Summary
Pre-registered study auditing whether general-purpose helpfulness rubrics can distinguish direct answer-giving from pedagogical guidance in LLM tutors. Uses deterministic detectors for answer leakage and next-turn independent work across three tutor models. Finds that helpfulness ratings conflate genuine pedagogical scaffolding with simply giving correct answers.
The work connects to broader discussions in AI and education around intelligent-tutoring-systems, automated-feedback, llm-evaluation, contributing to our understanding of how llm shapes educational practice.
Key Contributions
- Contributes empirical or theoretical advances relevant to the intelligent-tutoring-systems domain
- Published in 2026, reflecting the fast-moving landscape of AI in education research
- Engages with questions of intelligent tutoring and automated grading in educational contexts
Related Pages
Citation
APA: Shuyi Fan, Boyuan Deng, Mengyu Xu, Jiale Liu, Hongyang Zhang (2026). Rethinking LLM-Judged Helpfulness as a Pedagogy Signal: A Pre-Registered Audit Across Tutor Models. arXiv:2607.28128. cs.CL, cs.AI, cs.CY.