"Pathways to the Metaverse": Exploring the User Experience Mechanisms Driving Technology Acceptance in Virtual Lab Visits with an LLM-powered Avatar

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Authors: Xinyi Tu, Francesco Biondani, Danial Amin, Angelica Fabillar, Trang Thi Thu Xuan, Huma Bano Adeel, Sonja M.H. Tervola, Carlo Berlingeri, F. Fummi, Joni O. Salminen, Bernard Jim Jansen
Cited by: 1
References: 123

Abstract

Metaverse environments combined with large language models (LLMs) enable guided interaction through LLM-powered avatars that function as embodied conversational agents. In our study, we examined how scholars interact with an LLM-powered avatar modeled after a real professor during a virtual reality (VR) tour of a research lab. As little is known about how metaverse characteristics shape the user experience (UX) mechanisms that drive acceptance of such technologies, we conducted a 2 (avatar realism: abstract vs. hyperrealistic) × 2 (immersion: desktop vs. headset-based VR) within-subjects study (N= 30), where academic participants engaged in a virtual lab tour guided by the professor avatar. We conducted path analyses on three conceptual models and, based on the results, proposed the Virtual Lab Acceptance Model (VLAM), which features an experiential path (where perceived immersion increases empathy towards the avatar and task enjoyment) and a rational path (where perceived realism increases avatar credibility and task confidence). Flow states amplify these pathways by strengthening task experiences. Task enjoyment is the strongest predictor of behavioral intention. These findings inform HCI research on metaverse characteristics to drive technology acceptance through UX mechanisms, yielding design implications for developing LLM-powered avatars for virtual labs.

Fields of Study & Subjects

Virtual Reality Applications and Impacts Innovative Approaches in Technology and Social Development AI in Service Interactions

Publication details

Journal / source
Proceedings of the 31st International Conference on Intelligent User Interfaces
Publisher
ACM
Pages
1722–1741
Publication date
3 March 2026
Language
EN