# Mapping Student-AI Interaction Dynamics in Multi-Agent Learning Environments: Supporting Personalised Learning and Reducing Performance Gaps

arXiv: https://arxiv.org/abs/2506.02993 · HTML: https://mlsanity.com/paper/2506.02993/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2025.

## Built on by (most cited first, 4 of 4)

- [Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence](https://mlsanity.com/paper/2605.14266/) (2026)
- [Simulating Students' Java Programming Errors with Large Language Models](https://mlsanity.com/paper/2606.14113/) (2026)
- [AgentCAT: Simulating Computerized Adaptive Testing via Multi-Agent Large Language Models](https://mlsanity.com/paper/2606.21832/) (2026)
- [Evaluating Scaffolding-Oriented Multi-Agent Large Language Model System for Clinical Interview Training](https://mlsanity.com/paper/2609.10939/) (2026)

## Abstract (arXiv)

Multi-agent AI systems, which simulate diverse instructional roles such as teachers and peers, offer new possibilities for personalized and interactive learning. Yet, student-AI interaction patterns and their pedagogical implications remain unclear. This study explores how university students engaged with multiple AI agents, and how these interactions influenced cognitive outcomes (learning gains) and non-cognitive factors (motivation, technology acceptance). Based on MAIC, an online learning platform with multi-agent, the research involved 305 university students and 19,365 lines of dialogue data. Pre- and post-test scores, self-reported motivation and technology acceptance were also collected. The study identified two engagement patterns: co-construction of knowledge and co-regulation. Lag sequential analysis revealed that students with lower prior knowledge relied more on co-construction of knowledge sequences, showing higher learning gains and post-course motivation. In contrast, students with higher prior knowledge engaged more in co-regulation behaviors but exhibited limited learning improvement. Technology acceptance increased across all groups. These findings suggest that multi-agent AI systems can adapt to students' varying needs, support differentiated engagement, and reduce performance gaps. Implications for personalized system design and future research directions are discussed.