Abstract
Virtual Reality (VR) offers opportunities for interactive learning, yet how presence relates to efficacy depends on task design, system control, and learner characteristics. This study examined how presence, system task design (SYS), and learner traits—including baseline efficacy and GPT-based interaction— shape technology adoption (TAM) and technological efficacy (TE) in browser-based VR. Eighty students completed web-based VR scenarios, and data analyzed with structural equation and generalized additive models. The SEM model showed good fit (robust CFI = .95). Presence predicted adoption, which in turn predicted efficacy, but only when scaffolding was well calibrated. SYS, defined by task control and repetition, moderated this pathway: structured sequencing strengthened the presence→TAM link, whereas excessive repetition weakened it. Learners with higher initial efficacy reported stronger presence under lower interaction demands. GPT dialogue modestly enhanced social immersion. Qualitative feedback emphasized that coherent task design, rather than visual fidelity, mattered most for sustaining engagement.
Keywords
Immersive Learning; System Task Design; Presence; Technology Adoption
DOI
https://doi.org/10.21606/iasdr.2025.186
Citation
Mengoni, P., Shen, D.J., Li, Z., Wong, P.P.,and Lam, G.(2025) System-Moderated Immersion: Modeling Presence, Agency, and AI Interaction in VR Learning, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.186
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 3 - Design, Art & Technology
System-Moderated Immersion: Modeling Presence, Agency, and AI Interaction in VR Learning
Virtual Reality (VR) offers opportunities for interactive learning, yet how presence relates to efficacy depends on task design, system control, and learner characteristics. This study examined how presence, system task design (SYS), and learner traits—including baseline efficacy and GPT-based interaction— shape technology adoption (TAM) and technological efficacy (TE) in browser-based VR. Eighty students completed web-based VR scenarios, and data analyzed with structural equation and generalized additive models. The SEM model showed good fit (robust CFI = .95). Presence predicted adoption, which in turn predicted efficacy, but only when scaffolding was well calibrated. SYS, defined by task control and repetition, moderated this pathway: structured sequencing strengthened the presence→TAM link, whereas excessive repetition weakened it. Learners with higher initial efficacy reported stronger presence under lower interaction demands. GPT dialogue modestly enhanced social immersion. Qualitative feedback emphasized that coherent task design, rather than visual fidelity, mattered most for sustaining engagement.