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

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Publicado en:Education Sciences vol. 15, no. 8 (2025), p. 997-1020
Autor principal: Hong Sumin
Otros Autores: Moon Jewoong, Eom Taeyeon, Awoyemi, Idowu David, Hwang Juno
Publicado:
MDPI AG
Materias:
Teaching
Pedagogy
Usability
Instructional design
Teacher education
Cognitive load
Feedback
Preservice teachers
Virtual reality
Generative artificial intelligence
Case studies
Innovations
Simulation
Classrooms
Multimedia
Decision making
Learning
Mixed methods research
Quantitative analysis
Qualitative research
Computer Simulation
Authentic Learning
Literature Reviews
Instructional Innovation
Experiential Learning
Influence of Technology
Educational Methods
Educational Innovation
Educational Technology
Inplant Programs
Classroom Techniques
Feedback (Response)
Field Experience Programs
Information Transfer
Artificial Intelligence
Emotional Response
Affordances
Cognitive Ability
Learner Engagement
Educational Strategies
Educational Principles
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2227-7102
2076-3344
DOI:10.3390/educsci15080997
Fuente:Education Database