Designing Human–AI Collaboration for Hybrid Intelligence in Immersive Learning Environments: A Conceptual Framework

The shift toward hybrid intelligence in learning systems emphasizes the integration of human and AI cognitive capabilities into unified problem-solving processes. Yet, design principles for enabling such systems in immersive learning environments remain insufficiently understood. Immersive learning environments, realized through extended reality (XR), introduce unique affordances and challenges for embodied interaction, spatial communication, and co-presence that demand rethinking how collaboration unfolds. This conceptual paper proposes a framework and a set of design commitments for enabling sensible Human–AI collaboration for hybrid intelligence in immersive learning environments. Drawing on research and theories in Human–AI teaming and Human–AI collaboration, XR interaction design, learning sciences, and cognitive ergonomics, we identified four key dimensions of collaboration: collaborative agency and role distribution, shared attention and regulation, embodied and spatial interaction, and mutual intelligibility and adaptive support. We outline a conceptual framework describing how humans and AI can jointly achieve goals, negotiate roles, coordinate attention, and engage in knowledge co-construction within immersive learning spaces for hybrid intelligence. We further argue that immersive contexts require new forms of mutual intelligibility, spatial communication, and adaptive support to enable hybrid intelligence characterized by adaptive co-intelligence that improves learning processes in real time. Further, we advance a definition of hybrid intelligence specific to immersive learning that identifies three emergent properties: collaborative fluency, adaptive co-presence, and distributed knowledge growth. The paper closes with implications for researchers and practitioners and identifies constitutive design tensions that future work would navigate. Finally, this paper is conceptual in nature; the framework presented is offered as a theoretically grounded hypothesis for future empirical inquiry. Future research directions include validating the emergent properties through observational and experimental studies in actual XR environments and developing measurement tools adequate to the spatial, embodied, and real-time dimensions the framework identified.