Lunchtime Talk by Nia Nixon
What happens to collaboration when it is no longer only between humans, but between humans and AI?
Research on collaborative learning has long emphasized that successful teamwork depends on the interplay of cognitive, social, and affective processes. As AI systems begin to participate in team interactions, they introduce new forms of influence on how ideas are generated, how decisions are made, and how participation is distributed across team members. Yet there remain limited ways of studying these dynamics as they unfold in interaction.
This talk draws on a series of recent studies examining human–AI teaming in collaborative problem-solving contexts. An experimental platform, TRAIL, enables the systematic study of these questions by supporting controlled team interactions while capturing fine-grained traces of communication over time. This approach makes it possible to examine how AI participation relates to patterns of coordination, reasoning, and engagement within teams.
ABOUT THE SPEAKER: Nia Nixon’s research sits at the intersection of learning sciences, cognitive science, and artificial intelligence, with a focus on understanding the socio-cognitive and affective dynamics of collaborative problem solving. She develops computational approaches, including natural language processing and multimodal learning analytics, to study how teams coordinate, reason, and engage in real time.
Her recent work focuses on human–AI teaming, exploring how AI systems function as collaborators in group settings and how their presence reshapes participation, trust, and epistemic agency. She leads the development of TRAIL (Team Research and AI Integration Lab), an experimental platform for studying human–AI collaboration in controlled, data-rich environments.
Her work has been supported by the Jacobs Foundation, the Bill & Melinda Gates Foundation, the Spencer Foundation, the National Institutes of Health (NIH), and the National Science Foundation (NSF). She previously served as Vice President of the Society for Learning Analytics Research (SoLAR).
