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Teaching-over-solving diagnostic

Research Method · All research methods
Evidence · 4 claims (3 for, 1 mixed) · 3 studies (2 causal, 1 associational), q2–q3 · 0 of 3 report an effect size · 4 claims rest on one study

Description

The article proposes a lightweight diagnostic that reaggregates public tutoring benchmark scores into two composites: a solving composite (problem solving, solution correctness, mistake location, mistake correction) and a pedagogy composite (socratic questioning, scaffolding win rate, pedagogy instruction-following win rate, hard scaffolding, hard pedagogy instruction-following). For each model it defines "Gap(m) = Teach(m)−Solve(m)", where positive values indicate models relatively stronger on pedagogy than on solving. It is intended for education-oriented NLP work that lacks private classroom data.

Accounts

  • Teaching-over-solving diagnostic: separating answer-production ability from learning-support behavior via solving and pedagogy composites and their gap: The article proposes a lightweight diagnostic that reaggregates public tutoring benchmark scores into two composites: a solving composite (problem solving, solution correctness, mistake location, mistake correction) and a pedagogy composite (socratic questioning, scaffolding win rate, pedagogy instruction-following win rate, hard scaffolding, hard pedagogy instruction-following). For each model it defines "Gap(m) = Teach(m)−Solve(m)", where positive values indicate models relatively stronger on pedagogy than on solving. It is intended for education-oriented NLP work that lacks private classroom data. (Junyi Yao et al. (2026))
  • Report solving-oriented and pedagogy-oriented tutoring scores separately, with a disclosure-sensitive dimension penalizing full-solution giveaways: The article recommends that tutoring benchmark leaderboards stop collapsing task success and learning support into a single helpfulness score. It argues "benchmark reporting should preserve the distinction between solving strength and teaching strength instead of collapsing them into a single notion of helpfulness", and that benchmarks should include a disclosure-sensitive dimension penalizing giving away full solutions when the learner context calls for scaffolding. (Junyi Yao et al. (2026))
  • Taxonomy of high-quality strategies that foster understanding versus low-quality strategies that lead students to the solution: The article defines a two-category taxonomy of tutor moves in mathematics tutoring, drawing on prior work. High-quality strategies "foster students' understanding": prompting the student to explain, asking questions to guide thinking, and affirming the student's correct attempt. Low-quality strategies "lead students directly to the solution or passively engage students": asking the student to retry, giving away the answer/explanation, giving away the solution strategy, and encouraging the student in a generic way. Binary RoBERTa classifiers identified these strategies at scale with F1-scores between 0.65 and 0.90. (Rose E. Wang et al. (2025))

Claims

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Key Sources

  • Junyi Yao, Zihao Zheng, and Baichuan Li. (2026). Beyond Helpfulness: A Teaching-over-Solving Diagnostic for Measuring Educational Impact in LLM Tutors. arXiv preprint. https://arxiv.org/abs/2606.16206
  • Rose E. Wang, Ana T. Ribeiro, Carly D. Robinson, Susanna Loeb, and Dora Demszky. (2025). Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise. arXiv:2410.03017v2. https://arxiv.org/abs/2410.03017v2
Page metadata
Field Value
type research-method
id teaching-over-solving-diagnostic
title Teaching-over-solving diagnostic
description The article proposes a lightweight diagnostic that reaggregates public tutoring benchmark scores into two composites: a solving composite (problem solving, solution correctness, mistake location, mistake correction) and a pedagogy composite (socratic questioning, scaffolding win rate, pedagogy instr
status review
generated by process:settle-candidates on 2026-10-11
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