Immersion for AI: Immersive Learning with Artificial Intelligence

TL;DR

This paper explores how immersive learning theory can redefine AI as an active participant in cognitive ecologies, emphasizing design considerations for AI engagement and evolution beyond static models.

Contribution

It introduces a new perspective on AI as an immersive learner, integrating immersive learning dimensions into AI participation and proposing implications for AI training and collaboration.

Findings

AI can interpret narratives of data evolution

AI can make operational decisions in immersive environments

Design principles for AI engagement in cognitive ecologies

Abstract

This work reflects upon what Immersion can mean from the perspective of an Artificial Intelligence (AI). Applying the lens of immersive learning theory, it seeks to understand whether this new perspective supports ways for AI participation in cognitive ecologies. By treating AI as a participant rather than a tool, it explores what other participants (humans and other AIs) need to consider in environments where AI can meaningfully engage and contribute to the cognitive ecology, and what the implications are for designing such learning environments. Drawing from the three conceptual dimensions of immersion - System, Narrative, and Agency - this work reinterprets AIs in immersive learning contexts. It outlines practical implications for designing learning environments where AIs are surrounded by external digital services, can interpret a narrative of origins, changes, and structural developments in data, and dynamically respond, making operational and tactical decisions that shape human-AI collaboration. Finally, this work suggests how these insights might influence the future of AI training, proposing that immersive learning theory can inform the development of AIs capable of evolving beyond static models. This paper paves the way for understanding AI as an immersive learner and participant in evolving human-AI cognitive ecosystems.