Publication
Abstract
This study explores the historical development of Virtual Reality (VR) and Augmented Reality (AR) technologies and their applications in various fields, such as education, tourism, and consumer experiences. Through a review of relevant literature, the paper analyzes how VR and AR enhance user engagement and satisfaction by providing immersive and interactive experiences. In education, VR and AR are used to create vivid learning environments, promoting students' understanding and interest; in the tourism industry, these technologies enhance visitors' exploration and experience of destinations; in business, AR applications improve consumer shopping experiences and brand loyalty.
Despite the widespread application of these technologies facing challenges such as high costs and user adaptability issues, research shows their potential remains significant. Future research should focus on optimizing user experience, lowering technological barriers, and expanding application scenarios. In conclusion, the development of VR and AR technologies will drive innovation and transformation across industries, offering richer experiences in daily life.
Keywords
Virtual Reality (VR) , Augmented Reality (AR) , Immersion , Interaction .
Metadata
Disciplines: Computer Science
Cite This Article
APA Style
Lyu, S. (2024). The application of generative ai in virtual reality and augmented reality. Journal of Industrial Engineering and Applied Science, 2(6), 1-9. https://doi.org/10.70393/6a69656173.323339
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
FUNDING
INSTITUTIONAL REVIEW BOARD STATEMENT
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
INFORMED CONSENT STATEMENT
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
AUTHOR CONTRIBUTIONS
References
1.
Wang, C., Kang, D., Sun, H. Y., Qian, S. H., Wang, Z. X., Bao, L., & Zhang, S. H. (2024). MeGA: Hybrid Mesh-Gaussian Head Avatar for High-Fidelity Rendering and Head Editing. arXiv preprint arXiv:2404.19026.
2.
Wang, D. (Ed.). (2016). Information Science and Electronic Engineering: Proceedings of the 3rd International Conference of Electronic Engineering and Information Science (ICEEIS 2016), January 4-5, 2016, Harbin, China. CRC Press.
3.
Huang,S., Yang, H., Yao, Y., Lin, X.& Tu, Y. (2024). Deep adaptive interest network: personalized recommendation with context-aware learning. arXiv preprint arXiv:2409.02425.
4.
Z. Wang et al., ―Cdc-yolofusion: Leveraging cross-scale dynamic convolution fusion for visible-infrared object detection,‖ IEEE Transactions on Intelligent Vehicles, pp. 1–14, 2024.
5.
Yu, P., Xu, X., & Wang, J. (2024). Applications of Large Language Models in Multimodal Learning. Journal of Computer Technology and Applied Mathematics, 1(4), 108-116.
6.
Yang, J., Liu, J., Yao, Z., & Ma, C. (2024). Measuring digitalization capabilities using machine learning. Research in International Business and Finance, 70, 102380. doi:10.1016/j.ribaf.2024.102380
7.
Ma, B., Ma, B., Gao, M., Wang, Z., Ban, X., Huang, H., & Wu, W. (2021). Deep learning‐based automatic inpainting for material microscopic images. Journal of Microscopy, 281(3), 177-189.
8.
Zhang, H., Guo, J., Li, K., Zhang, Y., & Zhao, Y. (2024). Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder. arXiv preprint arXiv:2409.19754.
9.
Li, S., Lin, J., Shi, H., Zhang, J., Wang, S., Yao, Y., ... & Yang, K. (2024). DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction. arXiv preprint arXiv:2405.05518.
10.
Cao, J., Xu, R., Lin, X., Qin, F., Peng, Y., & Shao, Y. (2023). Adaptive receptive field U-shaped temporal convolutional network for vulgar action segmentation. Neural Computing and Applications, 35(13), 9593–9606. https://doi.org/10.1007/s00500-022-05938-1
11.
Shijie Liu, Kang Yan, Feiwei Qin, Changmiao Wang, Ruiquan Ge, Kai Zhang, Jie Huang, Yong Peng, and Jin Cao. Infrared image super-resolution via lightweight information split network. In International Conference on Intelligent Computing, pages 293–304. Springer, 2024.
12.
Jiang, H., Qin, F., Cao, J., Peng, Y., & Shao, Y. (2021). Recurrent neural network from adder’s perspective: Carry-lookahead RNN. Neural Networks, 144, 297–306. https://doi.org/10.1016/j.neunet.2021.07.011
13.
Yang, Y., Jin, Y., Tian, Q., Yang, Y., Qin, W., & Ke, X. (2024). Enhancing Gastrointestinal Diagnostics with YOLO-Based Deep Learning Techniques. Preprints. https://doi.org/10.20944/preprints202408.1202.v1
14.
Y. Jin, “Graphcnnpred: A stock market indices prediction using a graph based deep learning system,” arXiv preprint arXiv:2407.03760, 2024.
15.
Yu, P., Cui, V. Y., & Guan, J. (2021, March). Text classification by using natural language processing. In Journal of Physics: Conference Series (Vol. 1802, No. 4, p. 042010). IOP Publishing.
16.
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative ai. Business & Information Systems Engineering, 66(1), 111-126.
17.
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (No. w31161). National Bureau of Economic Research.
18.
Epstein, Z., Hertzmann, A., Investigators of Human Creativity, Akten, M., Farid, H., Fjeld, J., ... & Smith, A. (2023). Art and the science of generative AI. Science, 380(6650), 1110-1111.
19.
Bretos, M. A., Ibáñez-Sánchez, S., & Orús, C. (2024). Applying virtual reality and augmented reality to the tourism experience: a comparative literature review. Spanish Journal of Marketing-ESIC, 28(3), 287-309.
20.
Raji, M. A., Olodo, H. B., Oke, T. T., Addy, W. A., Ofodile, O. C., & Oyewole, A. T. (2024). Business strategies in virtual reality: a review of market opportunities and consumer experience. International Journal of Management & Entrepreneurship Research, 6(3), 722-736.
21.
Mergen, M., Graf, N., & Meyerheim, M. (2024). Reviewing the current state of virtual reality integration in medical education-a scoping review. BMC Medical Education, 24(1), 788.
22.
Lampropoulos, G., & Kinshuk. (2024). Virtual reality and gamification in education: a systematic review. Educational technology research and development, 1-95.
23.
Berkman, M. I. (2024). History of virtual reality. In Encyclopedia of computer graphics and games (pp. 873-881). Cham: Springer International Publishing.
24.
Omran, W., Ramos, R. F., & Casais, B. (2024). Virtual reality and augmented reality applications and their effect on tourist engagement: a hybrid review. Journal of Hospitality and Tourism Technology, 15(4), 497-518.
25.
Hidayat, R., & Wardat, Y. (2024). A systematic review of augmented reality in science, technology, engineering and mathematics education. Education and Information Technologies, 29(8), 9257-9282.
26.
Koumpouros, Y. (2024). Revealing the true potential and prospects of augmented reality in education. Smart Learning Environments, 11(1), 2.
27.
Wahid, A., Huda, M., Rohim, M. A., Ali, A. H., Kaspin, K. G., Fiqiyah, M., & Jima’ain, M. T. A. (2024, February). Augmented reality model in supporting instruction process: a critical review. In International Congress on Information and Communication Technology (pp. 69-83). Singapore: Springer Nature Singapore.
28.
Schultz, C. D., & Kumar, H. (2024). ARvolution: Decoding consumer motivation and value dimensions in augmented reality. Journal of Retailing and Consumer Services, 78, 103701.
29.
Rauschnabel, P. A., Felix, R., Heller, J., & Hinsch, C. (2024). The 4C framework: Towards a holistic understanding of consumer engagement with augmented reality. Computers in Human Behavior, 154, 108105.
30.
Wahyuanto, E., Heriyanto, H., & Hastuti, S. (2024). Study of the Use of Augmented Reality Technology in Improving the Learning Experience in the Classroom. West Science Social and Humanities Studies, 2(05), 700-705.
31.
Tao Y. SQBA: sequential query-based blackbox attack, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023). SPIE, 2023, 12803: 721-729.
32.
Tao Y. Meta Learning Enabled Adversarial Defense, 2023 IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE). IEEE, 2023: 1326-1330.
33.
Yiyi Tao, Yiling Jia, Nan Wang, and Hongning Wang. 2019. The FacT: Taming Latent Factor Models for Explainability with Factorization Trees. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 295–304.
34.
Yiyi Tao, Zhuoyue Wang, Hang Zhang, Lun Wang. 2024. NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training. arXiv:2409.09582.
35.
Luo, M., Zhang, W., Song, T., Li, K., Zhu, H., Du, B., & Wen, H. (2021, January). Rebalancing expanding EV sharing systems with deep reinforcement learning. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 1338-1344).
36.
Luo, M., Du, B., Zhang, W., Song, T., Li, K., Zhu, H., ... & Wen, H. (2023). Fleet rebalancing for expanding shared e-Mobility systems: A multi-agent deep reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3868-3881.
37.
Zhu, H., Luo, Y., Liu, Q., Fan, H., Song, T., Yu, C. W., & Du, B. (2019). Multistep flow prediction on car-sharing systems: A multi-graph convolutional neural network with attention mechanism. International Journal of Software Engineering and Knowledge Engineering, 29(11n12), 1727–1740.
38.
Sun, Y., & Ortiz, J. (2024). Machine Learning-Driven Pedestrian Recognition and Behavior Prediction for Enhancing Public Safety in Smart Cities. Journal of Artificial Intelligence and Information, 1, 51-57.
39.
Yaonian Zhong, Enhancing the Heat Dissipation Efficiency of Computing Units Within Autonomous Driving Systems and Electric Vehicles, J. World Journal of Innovation and Modern Technology, 2024, 7 (5): 100-104.
40.
Lin, W. (2024). A Review of Multimodal Interaction Technologies in Virtual Meetings. Journal of Computer Technology and Applied Mathematics, 1(4), 60-68.
41.
Lin, W. (2024). A Systematic Review of Computer Vision-Based Virtual Conference Assistants and Gesture Recognition. Journal of Computer Technology and Applied Mathematics, 1(4), 28-35.
42.
Luo, M., Zhang, W., Song, T., Li, K., Zhu, H., Du, B., & Wen, H. (2021, January). Rebalancing expanding EV sharing systems with deep reinforcement learning. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 1338-1344).
43.
Luo, M., Du, B., Zhang, W., Song, T., Li, K., Zhu, H., ... & Wen, H. (2023). Fleet rebalancing for expanding shared e-Mobility systems: A multi-agent deep reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3868-3881.
44.
Zhu, H., Luo, Y., Liu, Q., Fan, H., Song, T., Yu, C. W., & Du, B. (2019). Multistep flow prediction on car-sharing systems: A multi-graph convolutional neural network with attention mechanism. International Journal of Software Engineering and Knowledge Engineering, 29(11n12), 1727–1740.
45.
Sun, Y., & Ortiz, J. (2024). Machine Learning-Driven Pedestrian Recognition and Behavior Prediction for Enhancing Public Safety in Smart Cities. Journal of Artificial Intelligence and Information, 1, 51-57.
46.
Yaonian Zhong, Enhancing the Heat Dissipation Efficiency of Computing Units Within Autonomous Driving Systems and Electric Vehicles, J. World Journal of Innovation and Modern Technology, 2024, 7 (5): 100-104.
47.
Leong, H. Y., Gao, Y. F., Shuai, J., Zhang, Y., & Pamuksuz, U. (2024). Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation. arXiv preprint arXiv:2409.09324.
PUBLISHER'S NOTE
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
This work is licensed under a Creative
Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.