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Human–GenAI collaboration: a framework for engagement, self-regulation and ethical learning outcomes

Primary research

#493

T1new
Topic
unassigned (set during synthesis)
First seen
2026-07-21 07:15:55
Last seen
2026-07-21 07:15:55

Source raw items (1)

  • Semantic Scholar2026-07-21 07:15:18
    Human–GenAI collaboration: a framework for engagement, self-regulation and ethical learning outcomes

    Generative Artificial Intelligence is reshaping higher education by enabling new forms of collaboration between learners and intelligent systems. Drawing on Hybrid Intelligence and Self-Regulated Learning theories, this study proposes and empirically tests a Hybrid–Self-Regulation Model to explain how human–artificial intelligence collaboration influences engagement, self-regulation and learning outcomes. Specifically, the study examines how perceived collaborative artificial intelligence support, trust in artificial intelligence collaboration and artificial intelligence-enabled adaptive personalization drive collaborative engagement (CE), which in turn enhances self-monitoring (SM) and metacognitive reflection (MR). Using data from 307 students across six countries, the study examines how Perceived Collaborative AI Support (PCAS), Trust in AI Collaboration (TAIC) and AI-Enabled Adaptive Personalization (AIAP) drive CE, which in turn enhances SM and MR. The findings reveal that perceived collaborative artificial intelligence support, trust in artificial intelligence collaboration and artificial intelligence-enabled adaptive personalization significantly increase CE and CE strongly predicts SM and MR. MR further positively influences academic achievement, creativity and innovation and responsible and ethical problem-solving. Serial mediation analyses confirm that the impact of perceived collaborative artificial intelligence support and artificial intelligence-enabled adaptive personalization on learning outcomes is transmitted through CE, SM and MR. This study contributes theoretical clarity on human–artificial intelligence co-learning mechanisms and offers practical guidance for designing artificial intelligence-enhanced, ethically grounded learning ecosystems in higher education.