arXiv:2606.16206v1 Announce Type: cross Abstract: Large language models are increasingly proposed as educational tutors, yet stronger task-solving ability does not necessarily imply stronger learning support. Motivated by recent calls to measure the social impact of NLP systems in practice, we study whether public LLM tutoring benchmarks distinguish learning-supportive behavior from mere answer production. We propose a lightweight diagnostic based on the gap between solving-oriented and pedagogy-oriented benchmark performance. Using public MathTutorBench leaderboard results, we show that these
The proliferation of LLMs in educational contexts necessitates methods to evaluate their actual learning impact beyond mere task completion, reflecting a growing maturity in AI application assessment.
This research provides a critical diagnostic tool to assess the true pedagogical value of LLM tutors, distinguishing effective learning support from superficial answer generation, which is vital for developing impactful AI educational tools.
The explicit methodology for evaluating LLM tutors based on pedagogical support rather than just problem-solving ability will shift development priorities and benchmarks for AI in education.
- · AI ethicists
- · Educators
- · Students
- · LLM developers focused on pedagogy
- · LLM developers focused solely on task completion
- · Educational platforms using superficial LLM integration
Increased focus on 'explainable AI' and 'pedagogical AI' features in LLM development for education.
New open-source benchmarks and certifications emerge to validate the educational efficacy of AI tutors.
The development of 'AI-native' curricula specifically designed to leverage and optimize learning with pedagogically-sound AI tutors.
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