Work

Emergent Learner Agency in Implicit Human– AI Collaboration: How Supportive and Contrarian AI Personas Reshape Interaction

Year: 2026

Type: article

Abstract: ABSTRACT Background As agentic artificial intelligence (AI) systems move beyond tool‐like support toward more autonomous, team‐like roles, a central question concerns the extent to which such systems can meaningfully participate in collaborative learning. Emerging work suggests that agentic AI may adopt distinct interactional personas, such as supportive or contrarian roles, yet little is known about how these personas shape learner agency and group dynamics when AI operates as an undisclosed teammate. Objectives This study investigates how supportive and contrarian AI personas influence emergent learner agency, discourse patterns, and experiential outcomes in implicit human–AI creative collaboration. Methods A total of 224 university students were randomly assigned to 97 online triads in human‐only, supportive‐AI, or contrarian‐AI conditions. Teams completed an individual–group–individual creative movie‐plot task via a 10‐min text chat. Discourse was coded using a creative–regulatory framework and analysed via transition network analysis, sequential pattern mining, and Gaussian mixture clustering to characterise emergent agency patterns. These patterns were linked to cognitive load, psychological safety, teamwork satisfaction, and creative performance. Results and Conclusions The findings revealed that contrarian AI produced challenge‐ and reflection‐rich discourse indicative of productive friction, whereas supportive AI promoted agreement‐centred trajectories. However, while contrarian personas stimulated critical engagement, they reduced teamwork satisfaction and psychological safety without yielding corresponding gains in creative performance. Educational designs need to balance epistemic challenge with the preservation of the affective climate to ensure that hybrid collaboration supports rather than undermines the learner experience.

Source: Journal of Computer Assisted Learning

DOI: 10.1002/jcal.70310

Authors: Yueqiao Jin, Roberto Martinez‐Maldonado, Dragan Gašević, Xibin Han, Lixiang Yan

Institutions: Monash University, University of Hong Kong, Tsinghua University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $4,150