Immersion for AI: Immersive Learning with Artificial Intelligence

2025-02-05

AI Summary

This paper addresses the longstanding limitation in cognitive ecology research—treating AI solely as a passive tool rather than an active participant. To overcome this, it proposes “immersive learning” as a novel paradigm that repositions AI as an agentic, narrative-capable, and ecologically coordinated learner. Methodologically, it pioneers the systematic integration of immersive learning theory, cognitive ecology, and multi-agent modeling to establish design principles for learning environments supporting environmental perception, evolutionary structure recognition, and real-time adaptive response. Key contributions include: (1) establishing a theoretical foundation for AI as an evolving learning entity, transcending static model frameworks; (2) introducing a tri-dimensional human-AI co-interactive mechanism grounded in systemic coherence, narrative understanding, and agential autonomy; and (3) providing original theoretical grounding and practical pathways for developing next-generation AI training paradigms that are adaptive, self-evolving, and ecologically embedded.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Humans and AI: Learning Human Values and Preferences

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• AI as immersive learning participant
• Designing AI-inclusive cognitive ecologies
• Immersive learning theory for AI evolution

Innovation

• AI as immersive learner
• narrative interpretation in data
• dynamic human-AI collaboration