# Automatic Large Language Models Creation of Interactive Learning Lessons

arXiv: https://arxiv.org/abs/2506.17356 · HTML: https://mlsanity.com/paper/2506.17356/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://mlsanity.com/paper/2210.02406/) (2022)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Evaluating ChatGPT's Decimal Skills and Feedback Generation in a Digital Learning Game](https://mlsanity.com/paper/2306.16639/) (2023)
- [RAMO: Retrieval-Augmented Generation for Enhancing MOOCs Recommendations](https://mlsanity.com/paper/2407.04925/) (2024)
- [From First Draft to Final Insight: A Multi-Agent Approach for Feedback Generation](https://mlsanity.com/paper/2505.04869/) (2025)
- [SlideItRight: Using AI to Find Relevant Slides and Provide Feedback for Open-Ended Questions](https://mlsanity.com/paper/2505.04584/) (2025)
- [Beyond Search Engines: Can Large Language Models Improve Curriculum Development?](https://mlsanity.com/paper/2412.07422/) (2024)

## Built on by (most cited first, 2 of 2)

- [DrawSim-PD: Simulating Student Science Drawings to Support NGSS-Aligned Teacher Diagnostic Reasoning](https://mlsanity.com/paper/2602.01578/) (2026)
- [MisEdu-RAG: A Misconception-Aware Dual-Hypergraph RAG for Novice Math Teachers](https://mlsanity.com/paper/2604.04036/) (2026)

## Abstract (arXiv)

We explore the automatic generation of interactive, scenario-based lessons designed to train novice human tutors who teach middle school mathematics online. Employing prompt engineering through a Retrieval-Augmented Generation approach with GPT-4o, we developed a system capable of creating structured tutor training lessons. Our study generated lessons in English for three key topics: Encouraging Students' Independence, Encouraging Help-Seeking Behavior, and Turning on Cameras, using a task decomposition prompting strategy that breaks lesson generation into sub-tasks. The generated lessons were evaluated by two human evaluators, who provided both quantitative and qualitative evaluations using a comprehensive rubric informed by lesson design research. Results demonstrate that the task decomposition strategy led to higher-rated lessons compared to single-step generation. Human evaluators identified several strengths in the LLM-generated lessons, including well-structured content and time-saving potential, while also noting limitations such as generic feedback and a lack of clarity in some instructional sections. These findings underscore the potential of hybrid human-AI approaches for generating effective lessons in tutor training.