Abstract
The co-creation of games is a research area that has shown very promising results in identifying technological requirements. It is an approach where the researcher usually adopts the role of a participant observer, guiding the dynamics of co-creation acts. This situation limits the opportunities for replicability of co-creation methods by independent facilitators, which could elucidate the quality and improvement opportunities of these methods, contributing to their more widespread application. In this paper, we present a methodology that aims to overcome this limitation, allowing the replication of co-creation workshops by different independent facilitators. This methodology was conceived in the context of collecting relevant information for the design of an educational digital platform that intends to use gamified resources for adult education in digital health data literacy. Specifically, co-creation workshops were used to gain an overview of the difficulties of different age groups in this area and their perspective on which games would best address these difficulties. The workshops were conducted in five countries with planning oriented so that each country could have a different facilitator, not requiring the presence of the researcher who designed them. The challenge of this planning was to maintain the approach of the facilitators identical in all countries, as best one could. We present here the method adopted through its planning and materials designed for information collection, which included brainstorming using card sorting and game ideation with the use of templates. The analysis of replicability by independent facilitators was done by scrutinizing the produced elements, which allowed us to observe the aspects of coherence and divergence among the various facilitators. Thus, we conclude that this approach is a good starting point to overcome current limitations and identify possible lines of improvement.

Abstract

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

Abstract
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. © 2025 IEEE.

Abstract
The virtual reconstitution of Castelo de Vide, Portugal, within the FRONTOWNS project, highlights the challenges and successes of multidisciplinary collaboration in heritage preservation through 3D modeling. The goal was to reconstruct the town's urban evolution, focusing on its role as a border settlement from the 13th to 16th centuries. The project combined archaeological evidence, historical sources, and digital technologies like photogrammetry and 3D scanning. Co-creation workshops aligned diverse knowledge, leading to creative solutions that balanced historical accuracy and technical feasibility. Despite budget constraints, it produced a high-quality digital reconstitution with insights for future virtual heritage projects.

Abstract
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.