Search

The use of virtual reality (VR) in social work (SW) and pre-service teacher education has become a promising tool for skills development for students. A review of existing scholarship was conducted to provide an overview of the current landscape of VR in SW and pre-service teacher education. This analysis focused on the use of VR in fields that involved working with people, particularly people in crisis. After an initial review of 115 articles, 60 were identified as relevant to this study. Our analysis of recent trends in the literature emphasizes the potential of VR for library and information science (LIS) education. This literature review provides a basis for a study that examines how VR training approaches may be implemented to support LIS graduate students to build communication and de-escalation skills and confidence in interacting with patrons in crisis. Therefore, this literature review demonstrates the value of using VR and informs our proposed future project examining the viability and usability of VR training for this skill development using the social VR platform Mozilla Hubs.

Purpose: The purpose of this study is to explore whether immersive virtual reality (VR) can complement e-participation and help alleviate some major obstacles that hinder effective communication and collaboration. Immersive virtual reality (VR) can complement e-participation and help alleviate some major obstacles hindering effective communication and collaboration. VR technologies boost discussion participants' sense of presence and immersion; however, studying emerging VR technologies for their applicability to e-participation is challenging because of the lack of affordable and accessible infrastructures. In this paper, the authors present a novel framework for analyzing serious social VR engagements in the context of e-participation. Design/methodology/approach: The authors propose a novel approach for artificial intelligence (AI)-supported, data-driven analysis of group engagements in immersive VR environments as an enabler for next-gen e-participation research. The authors propose a machine-learning-based VR interactions log analytics infrastructure to identify behavioral patterns. This paper includes features engineering to classify VR collaboration scenarios in four simulated e-participation engagements and a quantitative evaluation of the proposed approach performance. Findings: The authors link theoretical dimensions of e-participation online interactions with specific user-behavioral patterns in VR engagements. The AI-powered immersive VR analytics infrastructure demonstrated good performance in automatically classifying behavioral scenarios in simulated e-participation engagements and the authors showed novel insights into the importance of specific features to perform this classification. The authors argue that our framework can be extended with more features and can cover additional patterns to enable future e-participation immersive VR research. Research limitations/implications: This research emphasizes technical means of supporting future e-participation research with a focus on immersive VR technologies as an enabler. This is the very first use-case for using this AI and data-driven infrastructure for real-time analytics in e-participation, and the authors plan to conduct more comprehensive studies using the same infrastructure. Practical implications: The authors' platform is ready to be used by researchers around the world. The authors have already received interest from researchers in the USA (Harvard University) and Israel and run collaborative online sessions. Social implications: The authors enable easy cloud access and simultaneous research session hosting 24/7 anywhere in the world at a very limited cost to e-participation researchers. Originality/value: To the best of the authors' knowledge, this is the very first attempt at building a dedicated AI-driven VR analytics infrastructure to study online e-participation engagements. [ABSTRACT FROM AUTHOR]