AUTHOR=Tan April , Dorneich Michael C. , Cotos Elena 

TITLE=Developing and evaluating the fidelity of virtual reality-artificial intelligence (VR-AI) environment for situated learning

JOURNAL=Frontiers in Virtual Reality

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/virtual-reality/articles/10.3389/frvir.2025.1587768

DOI=10.3389/frvir.2025.1587768

ISSN=2673-4192

ABSTRACT=IntroductionSocialization is crucial for facilitating disciplinary enculturation, yet traditional classroom instruction often lacks authentic socialization opportunities, limiting students’ exposure to their disciplinary communities. To address this gap, this study develops an immersive Virtual Reality-Artificial Intelligence (VR-AI) environment that simulates academic conference poster sessions. Learners interact with AI-driven agents, engaging in discussions and receiving real-time feedback on research communication. This study focuses on developing, operationalizing, and evaluating the fidelity of the VR-AI environment across four key dimensions: physical, functional, psychological, and social fidelity.MethodsTwenty participants tested the environment, completing two learning tasks: engaging with poster presenters and reflecting with a major professor. Fidelity was assessed using mixed methods, including presence questionnaires, workload assessments, behavioral observations, and semi-structured interviews. ResultsFindings indicate high physical and functional fidelity, with participants describing the environment as immersive and reflective of real-world academic settings. Psychological fidelity was also well represented, as learners engaged in cognitively demanding research discussions and rhetorical reflection. However, social fidelity remained a challenge, as AI agents struggled with conversational turn-taking and response length, reducing the authenticity of academic exchanges.DiscussionThese findings highlight the potential of VR-AI environments for disciplinary socialization while underscoring the need for refined AI-driven interaction designs to support more fluid, reciprocal dialogue.