When Peers Outperform AI (and When They Don't): Interaction Quality Over Modality

TL;DR

This study compares peer collaboration and AI assistance in undergraduate graph theory learning, highlighting how interaction quality influences engagement, curiosity, and learning outcomes, with implications for AI integration in education.

Contribution

It introduces a nuanced analysis of interaction quality's role in learning with AI versus peers, emphasizing dynamic patterns over individual traits.

Findings

High-quality peer interactions boost curiosity and engagement.

AI increases confidence but reduces curiosity and deep engagement.

Interaction quality predicts learning outcomes from discourse patterns.

Abstract

As AI increasingly enters the classroom, what changes when students collaborate with algorithms instead of peers? We analyzed 36 undergraduate students learning graph theory through peer collaboration (n=24) or AI assistance (n=12), using discourse analysis to identify interaction patterns shaping learning outcomes. Results reveal a collaboration quality divide: high-quality peer interactions generated curiosity and engagement that AI couldn't match, yet low-quality peer interactions performed worse than AI across dimensions. AI showed a paradoxical pattern, building confidence in knowledge while reducing curiosity and deeper engagement. Interaction quality emerged from dynamic patterns rather than individual traits, with early discourse markers predicting outcomes. Students treated AI as a transactional information source despite its collaborative design, revealing fundamental differences in human versus algorithmic engagement. Our findings suggest AI in education need not replace peer learning but can recognize struggle and support both peer and AI interactions toward productive learning experiences.