LessonBench-V1: A Benchmark Dataset for Evaluating AI Lesson Generation Agents
On the map: LLM agents and tool use → Educational agents and math problem solving
Abstract (arXiv)
Large Language Model (LLM) based AI educational content generation systems are increasingly being developed, yet no standardised benchmark exists to systematically evaluate them. This study introduces LessonBench-V1, a benchmark dataset comprising 647 human-written lessons paired with LLM-based reverse-engineered lesson plans across 240 STEM topics spanning mathematics, physics, chemistry, and computer science. The lessons are drawn from 97 trusted open sources, including LibreTexts, Brilliant.org and GeeksForGeeks. Each lesson plan is human-reviewed and produced through a pedagogically grounded methodology that synthesises Bloom's Taxonomy, Gagn'e's Events, Merrill's First Principles, and the 5E Instructional Model. The lesson plans capture 3,620 learning objectives with pedagogical metadata, enabling systematic, reproducible evaluation of lesson-generation AI agents and supporting further research. The study further proposes a three-dimensional evaluation pipeline for use with the dataset.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. Builds on 5 of its 13 references that are in the map. First posted on arXiv 12 Jun 2026.
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References · 5
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2023MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problemscited by 128
- 2025TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understandingcited by 20
- 2024Language Models as Science Tutorscited by 17
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