# Chrysalis: A Unified System for Comparing Active Teaching and Passive Learning with AI Agents in Education

arXiv: https://arxiv.org/abs/2510.05271 · HTML: https://mlsanity.com/paper/2510.05271/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 7 of 7)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Certifying LLM Safety against Adversarial Prompting](https://mlsanity.com/paper/2309.02705/) (2023)
- [Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming Education](https://mlsanity.com/paper/2309.14534/) (2023)
- [Task Supportive and Personalized Human-Large Language Model Interaction: A User Study](https://mlsanity.com/paper/2402.06170/) (2024)
- [Agents Are All You Need for LLM Unlearning](https://mlsanity.com/paper/2502.00406/) (2025)
- [Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education](https://mlsanity.com/paper/2412.15226/) (2024)
- [Exploring the Impact of an LLM-Powered Teachable Agent on Learning Gains and Cognitive Load in Music Education](https://mlsanity.com/paper/2504.00636/) (2025)

## Abstract (arXiv)

AI-assisted learning has seen a remarkable uptick over the last few years, mainly due to the rise in popularity of Large Language Models (LLMs). Their ability to hold long-form, natural language interactions with users makes them excellent resources for exploring school- and university-level topics in a dynamic, active manner. We compare students' experiences when interacting with an LLM companion in two capacities: tutored learning and learning-by-teaching. We do this using Chrysalis, an LLM-based system that we have designed to support both AI tutors and AI teachable agents for any topic. Through a within-subject exploratory study with 36 participants, we present insights into student preferences between the two strategies and how constructs such as intellectual humility vary between these two interaction modes. To our knowledge, we are the first to conduct a direct comparison study on the effects of using an LLM as a tutor versus as a teachable agent on multiple topics. We hope that our work opens up new avenues for future research in this area.