The utilization of conversational generative artificial intelligence (Gen AI) in learning is often seen as a double-edged sword that may lead to superficial learning. We designed and implemented a programming course focusing on collaboration between students and Gen AI. This study explores the dynamics of such collaboration, focusing on students’ communication strategies with Gen AI, perceived benefits, and challenges encountered. Data were collected from class observations, surveys, final reports, dialogues between students and Gen AI, and semi-structured in-depth interviews. The results showed that effective collaboration between students and Gen AI could enhance students’ meta-cognitive and self-regulated learning skills and positively impact human-to-human communication. This study further revealed the difficulties and individual differences in collaborating with Gen AI on complex learning tasks. Overall, collaborating with Gen AI as a learning partner, rather than just a tool, enables sustainable and independent learning, beyond specific learning tasks at a given time.

Understanding how students cognitively engage with generative artificial intelligence (GenAI) has become a pressing concern in educational technology research. This study investigated how motivational orientations and GenAI role perceptions are associated with the quality and structure of metacognitive engagement in authentic student-GenAI dialogues. Using purposive sampling, 24 Chinese undergraduates drawn from 20 academic disciplines participated in individual retrospective interviews. They narrated the context and reasoning behind 120 authentic GenAI interaction logs comprising 688 messages in total, of which 344 were student-initiated. Our analyses reveal that intrinsically motivated students predominantly positioned GenAI as an instructor or collaborator, whereas extrinsically motivated students disproportionately treated it as a replacement tool. Intrinsic motivation was associated with significantly higher engagement in higher-order metacognitive dimensions (evaluation and elaboration), forming reflective-iterative co-occurrence networks, while extrinsic motivation produced task-completion-oriented patterns concentrated in lower-order processing. Our analyses further suggest that students who saw GenAI as a collaborator had a complete metacognitive chain (i.e., high engagement) and those who saw GenAI as a replacement tool exhibited no higher-order metacognitive connections. Overall, these findings suggest that the metacognitive consequences of GenAI use are neither uniform nor inherent to the technology. They are systematically conditioned by motivational orientations and perceived GenAI roles during interactions.

The emergence of generative artificial intelligence (GenAI) poses profound challenges to unsupervised assessments in higher education. Yet little is known about how students use GenAI in after-class assessments, and which patterns of use are associated with cognitive disengagement or deeper learning. This study employed Biggs’ presage-process-product model to examine these processes in authentic assessment contexts. Using a qualitative methodology, we analysed 38 undergraduate students from Japan and China, examining their GenAI usage intentions, dialogue patterns and integration with other learning activities. Our findings show that student engagement with GenAI is not uniform but can be conceptualised as a spectrum bounded by cognitive outsourcing and cognitive reallocation. This spectrum adapts the classic surface and deep approaches to learning in GenAI-supported contexts. We also identified an efficiency paradox as a prevalent misalignment; whereby well-intentioned students default to passive processes and experience cognitive laziness and overreliance. The study operationalised and contextualised the 3P model in GenAI-supported assessment by articulating how presage factors shape distinct process patterns and perceived outcomes. It argues that interventions must move beyond technical skills to foster academic GenAI literacy and dialogue competence to provide explicit guidance that supports a shift from cognitive outsourcing towards more reflective, reallocation-oriented use. Implications for practice or policy: Educators should help students reconceptualise GenAI as a cognitive partner for exploring ideas, rather than just an upgraded search engine for retrieving answers. Guidance must move beyond technical skills to explicitly teach the metacognitive strategies and dialogue competence required to manage human-GenAI collaboration. Instructors must provide explicit, pedagogical guidance beyond prohibitions, helping students explore effective and ethical GenAI use within specific assessment contexts, shifting them away from outsourcing and towards reallocation.