Generative artificial intelligence augments social interactivity and learning outcomes: Advancing the framework of a scaffolded human–GenAI shared agency

Generative Artificial Intelligence (GenAI) functions not merely as a tool but an active collaborator in human knowledge construction; however, the Human-GenAI interaction dynamics is still underexplored. This study investigates Human-GenAI interaction profiles, the network interactivity and profile differences within a statistics learning community, as well as the underlying mechanisms linking Human-GenAI interaction to learning performance. We designed the Human–GenAI Inquiry and Problem-Solving Scaffold to foster shared agency between twenty-eight graduate students and GenAI across seven homework assignments in a sixteen-week advanced statistics course. Analytical approaches included k -modes clustering, social network analysis, and Partial Least Squares Structural Equation Modeling, complemented by case studies of interaction profiles. Three distinct Human-GenAI interaction profiles were identified: Human-GenAI collaborators, Peer collaborators with GenAI assistance, and Individual learners with late GenAI adoption. The network interactivity becomes cohesive with GenAI occupying the central hub role within the learning community. The models then demonstrate unique pathways through which Human-GenAI interaction influences learning performance, via degree centrality (number of direct connections) and peer nomination as helpers. The case studies highlight GenAI’s capability to augment human roles, encouraging deeper inquiry, expanding the depth of peer discussion, or promoting the exploration of diverse problem-solving strategies. These findings add value to theory and practice by providing empirical evidence for the framework of a scaffolded Human-GenAI shared agency, providing pedagogical implications to foster active student participation and harness GenAI’s potential to cultivate learner agency and symbiotic Human–GenAI knowledge construction. • Three human-AI interaction profiles were obtained from assignment collaboration. • Social network in statistics learning was modeled among human and GenAI. • Profiles show differences in centrality, interaction dynamics, and performance. • Degree and peer nomination mediate the association between profiles and learning. • Implications are made to foster human-GenAI shared agency with case studies.