Enhancing Immersion in Virtual Reality through Embodied Conversational Agents and Machine Learning: A Literature Review

Yihan Luo

doi:10.47297/taposatWSP2633-456916.20250605

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Enhancing Immersion in Virtual Reality through Embodied Conversational Agents and Machine Learning: A Literature Review

6卷第5期

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Enhancing Immersion in Virtual Reality through Embodied Conversational Agents and Machine Learning: A Literature Review
Enhancing Immersion in Virtual Reality through Embodied Conversational Agents and Machine Learning: A Literature Review
Southern Methodist University, Dallas, 75207, United States
2025年6卷第5期页码：74-79
纸质出版日期：2025-05-19
DOI：10.47297/taposatWSP2633-456916.20250605
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(2025). Enhancing Immersion in Virtual Reality through Embodied Conversational Agents and Machine Learning: A Literature Review. 科技理论与实践（英文版）, 6(5), 74-79.

Luo Yihan.Enhancing Immersion in Virtual Reality through Embodied Conversational Agents and Machine Learning: A Literature Review[J].Theory and Practice of Science and Technology,2025,06(05):74-79.
(2025). Enhancing Immersion in Virtual Reality through Embodied Conversational Agents and Machine Learning: A Literature Review. 科技理论与实践（英文版）, 6(5), 74-79. DOI： 10.47297/taposatWSP2633-456916.20250605.

Luo Yihan.Enhancing Immersion in Virtual Reality through Embodied Conversational Agents and Machine Learning: A Literature Review[J].Theory and Practice of Science and Technology,2025,06(05):74-79. DOI： 10.47297/taposatWSP2633-456916.20250605.

摘要

Abstract

Virtual Reality (VR) has revolutionized digital interaction by providing immersive environments that simulate real-world experiences. A critical component of successful VR applications is the user's sense of immersion

which encompasses presence

engagement

and realism. This literature review explores the integration of Embodied Conversational Agents (ECAs) and Machine Learning (ML) techniques in enhancing immersion within VR environments. By analyzing current research

this paper highlights key advancements

existing challenges

and potential future directions in this interdisciplinary domain. Particular attention is paid to the role of multimodal interaction

affective computing

and real-time adaptive learning

providing a comprehensive view of this rapidly evolving field.

关键词

Keywords

references

Beale, R., & Creed, C. (2009). Affective interaction: How emotional agents affect users. International Journal of Human-Computer Studies, 67(9), 755-776.

de Melo, C. M., Carnevale, P., & Gratch, J. (2014). The effect of expression of anger and happiness in computer agents on negotiations with humans. Autonomous Agents and Multi-Agent Systems, 30(1), 1-25.

Pfeiffer, T., Latoschik, M. E., & Wachsmuth, I. (2014). Evaluation of multimodal interfaces for virtual environments. Virtual Reality, 18(2), 101-115.

Slater, M., & Wilbur, S. (1997). A framework for immersive virtual environments (FIVE): Speculations on the role of presence in virtual environments. Presence: Teleoperators and Virtual Environments, 6(6), 603-616.

Wang, Q., Wu, H., & Liu, J. (2021). Intelligent tutoring systems with emotion recognition: A systematic review. Educational Technology Research and Development, 69(2), 879-905.

Stanney, K. M., Hale, K. S., Nahmens, I., & Kennedy, R. S. (2003). What to expect from immersive virtual environment exposure: Influences of gender, body mass index, and past experience. Human Factors, 45(3), 504-520.

Bailenson, J. N., Blascovich, J., Beall, A. C., & Loomis, J. M. (2003). Interpersonal distance in immersive virtual environments. Personality and Social Psychology Bulletin, 29(7), 819-833.

Rizzo, A. S., & Koenig, S. T. (2017). Is clinical virtual reality ready for primetime? Neuropsychology, 31(8), 877-899.

Bailenson, J. (2018). Experience on demand: What virtual reality is, how it works, and what it can do. W. W. Norton & Company.

Zhao, J., & Fessler, J. A. (2020). Federated learning with adaptive feature compression for virtual reality applications. IEEE Transactions on Multimedia, 23, 3041-3055.

Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A., Yogamani, S., & Perez, P. (2021). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(7), 4909-4926.

Liu, Y., & Matwin, S. (2019). Multimodal sentiment analysis: From words to speech and facial expressions. Information Fusion, 49, 1-12.

Huynh, D. Q., & Nguyen, D. T. (2020). Eye tracking in virtual reality: A review of challenges and opportunities. Frontiers in Virtual Reality, 1, 12.

Kellnhofer, P., Matusik, W., & Pfister, H. (2019). Real-time gaze estimation using a consumer-grade VR headset. ACM Transactions on Graphics, 38(4), 1-10.

Sra, M., Garrido-Jurado, S., & Schmandt, C. (2016). Procedurally generated virtual reality from 3D reconstruction. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 3576-3587.

Madary, M., & Metzinger, T. K. (2016). Real virtuality: A code of ethical conduct. Recommendations for good scientific practice and the consumers of VR technology. Frontiers in Robotics and AI, 3, 3.

Meta Reality Labs. (2021). Social presence in virtual reality: Advancements and applications. Meta Research Report.

MIT Media Lab. (2020). Reinforcement learning for adaptive embodied conversational agents. MIT Technical Reports.

Stanford Virtual Human Interaction Lab. (2021). Empathy at scale: VR applications for social good. Stanford Reports in Virtual Reality.

IEEE Virtual Reality Community. (2023). Standardizing presence measurement in immersive virtual environments. IEEE VR White Paper.

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