Chrysalis: A Unified System for Comparing Active Teaching and Passive Learning with AI Agents in Education
On the map: LLM agents and tool use → Educational agents and math problem solving
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.
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 7 of its 30 references that are in the map. First posted on arXiv 6 Oct 2025.
Builds on
7 of 30 references.
Closest ancestors · 1
Same direction, newest first
- 2023Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming Education25 Sep
Other references · 6
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2023Certifying LLM Safety against Adversarial Promptingcited by 248
- 2024Task Supportive and Personalized Human-Large Language Model Interaction: A User Studycited by 17
- 2025Agents Are All You Need for LLM Unlearningcited by 9
- 2024Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming educationcited by 8
- 2025Exploring the Impact of an LLM-Powered Teachable Agent on Learning Gains and Cognitive Load in Music Educationcited by 4
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework
- 2025LLM Agents for Education: Advances and Applications
- 2025Simulating Students with Large Language Models: A Review of Architecture, Mechanisms, and Role Modelling in Education with Generative AI
- 2026Building AI Companions that Prioritise Learning over Performance
- 2025Towards AI Agents for Course Instruction in Higher Education: Early Experiences from the Field
- 2025TutorGym: A Testbed for Evaluating AI Agents as Tutors and Students
- 2026Enhancing LLM Problem Solving via Tutor-Student Multi-Agent Interaction
- 2025A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents