Mapping Student-AI Interaction Dynamics in Multi-Agent Learning Environments: Supporting Personalised Learning and Reducing Performance Gaps
Journal:
arXiv
Published Date:
Jun 3, 2025
Abstract
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.
