This research uses AI to identify student communication roles in team projects, showing how these roles evolve and can predict peer recognition and team performance.

In plain terms

It's hard to understand how students work together in teams, especially their communication roles, and existing computer-based methods often miss insights from educational theories. To address this, researchers observed students in a university computer science course, analyzing over 6,000 Slack messages from 55 students in 18 teams. They applied an existing educational framework to identify eight distinct communication roles, like "initiator" or "supporter." To scale this, they tested if large language models (LLMs) – AI programs skilled at understanding human language – could accurately identify these roles, finding they could approximate human expert labels. They discovered that students play a more varied set of roles as a project progresses, and different roles become more active at various stages of team work. Using these AI-identified roles, they could successfully predict which students would be recognized by their peers and even predict improvements in team performance in a separate dataset.