From Tool to Co-Learner: Exploring Student Engagement With GenAI Through the Lens of Social Constructivism

This study investigates how students engage with GenAI during a business data analysis assessment, drawing on Social Constructivist Theory and the human–AI co-agency model. Within the assessment, students used GenAI tools to support their data analysis and reflected on their experiences by comparing AI-generated and manually derived results. Thematic analysis of 258 students’ reflection, triangulated with academic performance data, revealed four key themes: epistemic beliefs, functional cognitive engagement, reflective metacognitive learning, and Human–GenAI co-agency for strategic foresight. Students demonstrated distinct patterns of engagement across performance groups. Higher-performing students approached GenAI as a collaborative partner, engaging in iterative prompt refinement, demonstrating critical evaluation of outputs, and exhibiting strong ethical awareness. In contrast, lower-performing students often showed polarised epistemic beliefs, limited critical reflection, and minimal iteration – accepting or rejecting GenAI outputs prematurely. These findings highlight the role of scaffolded reflection and prompt engineering in enabling students to develop deeper analytical and evaluative capacities. By reconceptualising GenAI as an active co-learner rather than a passive tool, this study extends Social Constructivism perspectives to accommodate emerging forms of human–GenAI interaction. Drawing on rich, in-situ qualitative evidence embedded within an authentic learning context, it offers new insight into how students’ beliefs and strategies shape their engagement with GenAI. The study also emphasises the need for differentiated pedagogical designs that cultivate AI literacy, narrow digital divide, and support ethical, adaptive use of GenAI in higher education.