Year: 2026
Type: article
Abstract: Educational Virtual Reality (VR) provides immersive learning environments, yet most contemporary applications rely on pre-scripted Non-Player Characters (NPCs) that offer limited personalization and rigid interaction paths. This study presents the technical implementation and evaluation of TUMSphere, a VR orientation platform designed to facilitate the academic and cultural transition of international students. We propose a modular architecture that integrates Large Language Models (LLMs) with Unreal Engine via the Conversational AI (Convai) platform, enabling embodied NPCs to provide real-time speech recognition, context-aware dialogue, and autonomous spatial navigation. To validate this approach, a mixed-methods user study ( N = 24) was conducted with international students to assess system latency, usability, and pedagogical efficacy. Results demonstrate a high System Usability Scale (SUS) score of 76.4 ( SD = 12.5) and robust task completion rates, reaching 100% for spatial navigation and 96% for information retrieval. While technical benchmarking revealed an average end-to-end latency of 2.90s for complex, retrieval-heavy queries, qualitative findings indicate that users find this “latency-presence trade-off” acceptable in exchange for the pedagogical benefits. Crucially, participants reported a significant reduction in social anxiety when practicing language and administrative queries with AI agents compared to human interlocutors. These findings suggest that embodied, generative AI NPCs can serve as a scalable, low-pressure “social sandbox” that effectively redefines student support systems and orientation strategies in higher education.
Source: Frontiers in Computer Science
Authors: Santiago Berrezueta-Guzman, Stefan Wagner
Institution: Technical University of Munich
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,418
Funder: Technische Universität München
OpenAlex ID: W7135018953