AHCI RESEARCH GROUP

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proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2025

Hong, S.; Moon, J.; Eom, T.; Awoyemi, I. D.; Hwang, J.

Generative AI-Enhanced Virtual Reality Simulation for Pre-Service Teacher Education: A Mixed-Methods Analysis of Usability and Instructional Utility for Course Integration Journal Article

In: Education Sciences, vol. 15, no. 8, 2025, ISSN: 22277102 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).

Abstract | Links | BibTeX | Tags: Generative AI, Preservice teachers, simulation-based learning, teacher education, virtual reality simulation

@article{hong_generative_2025,

title = {Generative AI-Enhanced Virtual Reality Simulation for Pre-Service Teacher Education: A Mixed-Methods Analysis of Usability and Instructional Utility for Course Integration},

author = {S. Hong and J. Moon and T. Eom and I. D. Awoyemi and J. Hwang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105014401165&doi=10.3390%2Feducsci15080997&partnerID=40&md5=959944ad02f6047fd69e2e5bb964fbc3},

doi = {10.3390/educsci15080997},

issn = {22277102 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Education Sciences},

volume = {15},

number = {8},

abstract = {Teacher education faces persistent challenges, including limited access to authentic field experiences and a disconnect between theoretical instruction and classroom practice. While virtual reality (VR) simulations offer an alternative, most are constrained by inflexible design and lack scalability, failing to mirror the complexity of real teaching environments. This study introduces TeacherGen@i, a generative AI (GenAI)-enhanced VR simulation designed to provide pre-service teachers with immersive, adaptive teaching practice through realistic GenAI agents. Using an explanatory case study with a mixed-methods approach, the study examines the simulation’s usability, design challenges, and instructional utility within a university-based teacher preparation course. Data sources included usability surveys and reflective journals, analyzed through thematic coding and computational linguistic analysis using LIWC. Findings suggest that TeacherGen@i facilitates meaningful development of teaching competencies such as instructional decision-making, classroom communication, and student engagement, while also identifying notable design limitations related to cognitive load, user interface design, and instructional scaffolding. This exploratory research offers preliminary insights into the integration of generative AI in teacher simulations and its potential to support responsive and scalable simulation-based learning environments. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {Generative AI, Preservice teachers, simulation-based learning, teacher education, virtual reality simulation},

pubstate = {published},

tppubtype = {article}

}