Abstract
Immersive learning research is a field of study that emphasizes diversity of scholarship and subject areas. This diversity presents a challenge for understanding the breadth and depth of the field of immersive learning, a challenge that led to the Immersive Learning Research Network’s call for the community of immersive learning researchers to develop a conceptual framework supporting a common understanding of this diverse field - The Immersive Learning Knowledge Tree. However, this structure has not had its underlying assumptions validated by the larger, diverse community of immersive learning researchers and practitioners. Thus, we developed, validated, and disseminated across associations of the field a questionnaire for analyzing the assumptions, structure, and relevance of the Knowledge Tree proposal. Early results point towards overwhelming agreement from the community on the premise that the field of immersive learning research is muddled/fragmented, the current knowledge partially disjointed, specifically among different disciplines (Q3), due to its interdisciplinary nature. There are also strong indications supporting the premise that researchers active in the field of immersive learning research desire to combine their efforts with others. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Self-regulation of learning (SRL) plays a decisive role in learning success but characterizing learning environments that facilitate development of SRL skills constitutes a great challenge. Given the growing interest in Immersive Learning Environments (ILE), we sought to understand how ILE are built with attention to SRL, via a literature review of pedagogical uses, practices and strategies with ILE that have an explicit focus on SRL. From a final corpus of 25 papers, we collected 134 extracts attesting use of ILE for SRL. We classified and mapped them using the Beck, Morgado & O’Shea framework and its three dimensions of the immersion phenomenon: system, narrative and challenge. There is a predominance of uses of ILE for SRL aligned with Challenge-based immersion: Skill Training, Collaboration, Engagement, and Interactive Manipulation and Exploration. In contrast, uses aligned with System-based immersion (Emphasis, Accessibility, Seeing the Invisible) were not identified. There were few cases of use of Narrative-based immersion. Uses combining the three dimensions of immersive had residual prevalence. We concluded that there is greater tendency in studies of SRL in ILE to enact active roles (aligned with the Challenge dimension of immersion). The low prevalence of Narrative immersion and System immersion evidence gaps in the diversity of pedagogical uses of ILE to develop SRL, which indicate opportunities for research and creation of innovative educational practices. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The research areas of the Immersive Learning community cover many different interests and perspectives on teaching and learning with immersive technologies. Based on existing efforts to map the field of research, we gathered 35 participants at the iLRN 2022 conference during an open hybrid workshop. These volunteers formed expert groups focusing on five possible perspectives on Immersive Learning. The expert groups gathered and summarized possible research questions with regards to an “Agenda 2030”, meaning the most intriguing questions that should be addressed during the years to come. We let all participants vote on these research endeavors regarding their academic value and importance for the community. As a results, we gathered a total of 23 ranked questions. These questions were subsumed into ten topics forming a Research Agenda for Immersive Learning 2030 (RAIL.2030). © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The manual monitoring of refilling stations in industrial environments can lead to inefficiencies and errors, which can impact the overall performance of the production line. In this paper, we present an unsupervised detection pipeline for identifying refilling stations in industrial environments. The proposed pipeline uses a combination of image processing, pattern recognition, and deep learning techniques to detect refilling stations in visual data. We evaluate our method on a set of industrial images, and the findings demonstrate that the pipeline is reliable at detecting refilling stations. Furthermore, the proposed pipeline can automate the monitoring of refilling stations, eliminating the need for manual monitoring and thus improving industrial operations’ efficiency and responsiveness. This method is a versatile solution that can be applied to different industrial contexts without the need for labeled data or prior knowledge about the location of refilling stations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Artificial intelligence (AI) has gained significant evolution in recent years that, if properly harnessed, may meet or exceed expectations in a wide range of application fields. However, because Machine Learning (ML) models have a black-box structure, end users frequently seek explanations for the predictions made by these learning models. Through tools, approaches, and algorithms, Explainable Artificial Intelligence (XAI) gives descriptions of black-box models to better understand the models’ behaviour and underlying decision-making mechanisms. The AI development in companies enables them to participate in Industry 4.0. The need to inform users of transparent algorithms has given rise to the research field of XAI. This paper provides a brief overview and introduction to the subject of XAI while highlighting why this topic is generating more and more attention in many sectors, such as industry. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract