Research on Dual Impact of AI-VR Integrated Immersive STEM Teaching Model on Learning Motivation and Academic Performance

The development of artificial intelligence (AI) and virtual reality (VR) technologies is developing rapidly, and their application in education has also brought new opportunities to improve STEM (science, technology, engineering and mathematics) teaching and learning experience. Furthermore, this study introduces a new AI-VR fusion immersive teaching model to increase students' learning motivation and enhance their academic achievements. This model is based on the existing AI personalized learning system and VR immersive education model, combined with the adaptive AI content recommendation engine and personalized VR learning environment, dynamically adjust the teaching content and difficulty according to students' learning preferences and cognitive characteristics. By not only monitoring real-time responses of students including emotional and behavioral metrics for adjusting the immersive environment, but also using it as a way to ensure active student participation and retention of knowledge while enhancing the experience during learning sessions. We found that the AI-VR model, implemented in STEM modules and across different grades, was effective as demonstrated in the experimental results: compared to traditional teaching methods.

Authors

Institutions

Publication Details

Published
2025-04-18
DOI
https://doi.org/10.1145/3746469.3746515
Citations
1
Primary Topic
Advanced Technologies in Various Fields
Type
article
Field-Weighted Citation Impact
2.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on Dual Impact of AI-VR Integrated Immersive STEM Teaching Model on Learning Motivation and Academic Performance

Qi Bin Feng, Jianyi Li
1 citations
Advanced Technologies in Various Fields
2.48
article

Research on Dual Impact of AI-VR Integrated Immersive STEM Teaching Model on Learning Motivation and Academic Performance

Qi Bin Feng, Jianyi Li
article en
1 citations

Abstract

The development of artificial intelligence (AI) and virtual reality (VR) technologies is developing rapidly, and their application in education has also brought new opportunities to improve STEM (science, technology, engineering and mathematics) teaching and learning experience. Furthermore, this study introduces a new AI-VR fusion immersive teaching model to increase students' learning motivation and enhance their academic achievements. This model is based on the existing AI personalized learning system and VR immersive education model, combined with the adaptive AI content recommendation engine and personalized VR learning environment, dynamically adjust the teaching content and difficulty according to students' learning preferences and cognitive characteristics. By not only monitoring real-time responses of students including emotional and behavioral metrics for adjusting the immersive environment, but also using it as a way to ensure active student participation and retention of knowledge while enhancing the experience during learning sessions. We found that the AI-VR model, implemented in STEM modules and across different grades, was effective as demonstrated in the experimental results: compared to traditional teaching methods.

Jianghan University (CN), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 9%
Advanced Technologies in Various Fields
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Research on Dual Impact of AI-VR Integrated Immersive STEM Teaching Model on Learning Motivation and Academic Performance — Qi Bin Feng, Jianyi Li · (2025) | TGRS Research Map | TGRS