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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. © 2025 Elsevier B.V., All rights reserved.

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This paper presents a solution for visualizing oil spills at sea by combining satellite data with virtual choreographies. The system enables dynamic, interactive visualization of oil slicks, reflecting their shape, movement, and interaction with environmental factors like currents and wind. High resolution geospatial data supports a multiplatform experience with aerial and underwater perspectives. This approach promotes independence, interoperability, and multiplatform compatibility in environmental disaster monitoring. The results validate virtual choreographies as effective tools for immersive exploration and analysis, offering structured data narratives beyond passive visualization especially valuable for mixed reality applications.

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Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.

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Self and co-regulation of learning (SCRL) are strategies that students can adopt to become more active and committed to their learning. Encouraging students to adopt these strategies is a challenge for teachers that can be met by using narratives as a teaching resource. To support teachers in this process, we present a method for evaluating, classifying, and reflecting on excerpts from immersive narratives for SCRL, so they objectively base their decision-making. The method was developed as an artifact of Design Science Research (DSR). In the Design stage of DSR, a 4-stage scheme was developed, and 38 criteria were described to identify and classify narratives that guide or encourage students to adopt SCRL strategies. In the DSR demonstration stage, we tested the method in an asynchronous e-learning curricular unit in Portuguese higher education, which uses a narrative-oriented immersive learning approach for SCRL, called e-SimProgramming. The results show that the graphic visualization of the classification made it possible to perceive the occurrence of the SCRL categories in the narratives, enabling the teacher to be inspired and reflect on the categories to be enhanced for necessary changes in the narrative in line with their pedagogical objectives.

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