The metaverse has transitioned from a science fiction term to a rapidly growing area of research and application, with potential uses in education, professional training, social events, and the virtual economy. However, despite this progress, a fully realized and functional metaverse is not yet available, and its development still requires a clear understanding and definition of the research directions to follow. Nonetheless, the metaverse topic does not start from scratch; it shares its foundations with Virtual Environments (VEs), which represent the Virtual Reality applications' core. In this paper, we built on top of the knowledge given by the history of the two topics to present a scoping review of the historical development of research in VEs and the metaverse from the 1990s to early 2024, analyzing 352 papers from the Scopus database. We aimed to offer a comprehensive understanding of how past research informs the present and future directions of the metaverse. Our findings revealed that the metaverse, while emerging as a distinct research area in recent years, is deeply rooted in the history of VEs, with many of its concepts and technologies deriving from earlier work in the field. We also identified new, underexplored trends within the metaverse research, proposing a future research agenda informed by the shared history of the two topics.

The metaverse, a 3D immersive digital environment, is gaining significant interest due to its ability to connect people globally engaging and immersively, thanks to recent technological advancements. Developing high-quality 3D models is crucial for achieving realism and immersivity in the metaverse. However, this process is complex and resource-intensive, demanding specialized skills and substantial time. The emergence of novel automation tools and technologies, such as photogrammetry, which uses computer vision algorithms to reconstruct 3D models from 2D images, is beginning to address such challenges. Our research focused on analyzing the current state of such technologies in automating 3D scenes for the metaverse, comparing them to traditional manual modeling techniques. We conducted an experiment in which we built the same 3D scene using two techniques: a manual approach with Blender and a photogrammetry approach exploiting the Polycam tool on a mobile device. Our results have provided insights into the main strengths and limitations of using 3D automation techniques. The photogrammetry approach has significantly sped up the entire process, producing textures and models that accurately replicate real objects. However, it cannot wholly replace manual modeling approaches, without which it is impossible to obtain complete and efficient models. Lessons learned will serve as a foundation to guide developers in developing 3D scenes for the metaverse.

Context: The term metaverse refers to a persistent, virtual, three-dimensional environment where individuals may communicate, engage, and collaborate. One of the most multifaceted and challenging use cases of the metaverse is education, where educators and learners may require multiple technical, social, psychological, and interaction instruments to accomplish their learning objectives. While the characteristics of the metaverse might nicely fit the problem’s needs, our research points out a noticeable lack of knowledge into (1) the specific requirements that an educational metaverse should actually fulfill to let educators and learners successfully interact towards their objectives and (2) how to design an appropriate educational metaverse for both educators and learners.
Objective: In this paper, we aim to bridge this knowledge gap by proposing SENEM, a novel software engineering-enabled educational metaverse. We first elicit a set of functional requirements that an educational metaverse should fulfill.
Method: In this respect, we conduct a literature survey to extract the currently available knowledge on the matter discussed by the research community, and afterward, we assess and complement such knowledge through semi-structured interviews with educators and learners. Upon completing the requirements elicitation stage, we then build our prototype implementation of SENEM, a metaverse that makes available to educators and learners the features identified in the previous stage. Finally, we evaluate the tool in terms of learnability, efficiency, and satisfaction through a Rapid Iterative Testing and Evaluation research approach, leading us to the iterative refinement of our prototype.
Results: Through our survey strategy, we extracted nine requirements that guided the tool development that the study participants positively evaluated.
Conclusion: Our study reveals that the target audience appreciates the elicited design strategy. Our work has the potential to form a solid contribution that other researchers can use as a basis for further improvements.

The metaverse represents a persistent, online 3D universe where people can interact, socialize, and work toward common goals. Education represents a key application domain, as it has the potential to enhance experiential learning and collaboration between learners and between learners and educators. However, challenges to the widespread adoption of educational metaverses persist. This paper focuses on emotional isolation, i.e., the feeling of emotional disconnection or loneliness, which can hinder learners’ motivation and participation. Machine learning-enabled emotional recognition systems have the potential to address this challenge, offering educators with feedback on the emotional states of learners within the metaverse. Yet, the integration of emotion recognition systems raises ethical concerns regarding consent, privacy, and algorithmic bias. In this paper, we first conduct a literature review on the ethical considerations surrounding the deployment of emotion recognition technology within educational metaverses. Then, we report on the implementation of these guidelines within SENEM, an educational metaverse platform available in the literature. Through this research, we aim to contribute to the responsible deployment of emotion recognition technology in educational settings, ultimately fostering a supportive and inclusive learning environment for all learners.

In the post-pandemic era, more and more people are turning to virtual spaces for entertainment, work, education, and social interaction. Despite this enthusiasm, serious concerns have arisen regarding the accessibility and inclusion of people with disabilities in educational settings and how Artificial Intelligence (AI) approaches can mitigate these issues in the new digital realm. In this article, we conduct a systematic literature review (SRL) to identify the set of challenges that people and students with disabilities encounter when they are interested in accessing the metaverse. We collect solutions proposed in the literature to provide a comprehensive understanding of the accessibility of current metaverse platforms. We distill a number of takeaway messages encouraging researchers and practitioners to further work on the proposed solution, leveraging AI to monitor and enable people with disabilities to fully enjoy the metaverse experience.

Software engineering is a human-centered activity involving various stakeholders with different backgrounds that have to communicate and collaborate to reach shared objectives. The emergence of conflicts among stakeholders may lead to undesired effects on software maintainability, yet it is often unavoidable in the long run. Community smells, i.e., sub-optimal communication and collaboration practices, have been defined to map recurrent conflicts among developers. While some community smell detection tools have been proposed in the recent past, these can be mainly used for research purposes because of their limited level of usability and user engagement. To facilitate a wider use of community smell-related information by practitioners, we present CADOCS, a client-server conversational agent that builds on top of a previous community smell detection tool proposed by Almarini et al. to (1) make it usable within a well-established communication channel like Slack and (2) augment it by providing initial support to software analytics instruments useful to diagnose and refactor community smells. We describe the features of the tool and the preliminary evaluation conducted to assess and improve robustness and usability.