From Wandering to Collaboration: Discourse Patterns in Middle School Generative AI Use

Generative artificial intelligence (AI) has become a prominent presence in classrooms, yet relatively little is known about how students actually engage with such tools in authentic school contexts. This study examines more than 17,000 messages across 1,512 conversations from 484 middle school students using a classroom-based generative AI writing tutor. We extracted linguistic, cognitive, and interactional features, reduced dimensionality with principal component analysis, and applied clustering to identify conversation and student-level patterns of engagement. Results revealed five conversation profiles—ranging from directive to reflective dialogue—and four student profiles, including collaborators, transactional tool users, independent thinkers, and chatters. These patterns aligned with both pedagogical intent and individual orientation, underscoring that student–AI dialogue is heterogeneous but systematic. Findings contribute empirical evidence to debates about generative AI in education and provide a methodological framework for analyzing human–AI interaction in learning analytics