Inteligencia artificial adaptativa para optimizar experiencias inmersivas en realidades extendidas XR

Autores/as

  • Carlos Araujo Mejía Universidad Icesi
  • Luis Mejía Puig University of Florida
  • Daniel Gómez Marín Universidad Icesi

DOI:

https://doi.org/10.26507/paper.4809

Palabras clave:

Realidades extendidas, realidad virtual, realidad aumentada, inteligencia artificial, entornos adaptativos, HCI

Resumen

Este artículo presenta una revisión sistemática sobre el uso de inteligencia artificial adaptativa para optimizar experiencias inmersivas en entornos de realidad extendida (XR), abarcando tecnologías como la realidad virtual, aumentada y mixta. La investigación identifica tres ejes clave de personalización: 1. monitoreo del estado del usuario, 2. personalización conversacional mediante modelos generativos, y 3. modulación dinámica del entorno inmersivo.

Los hallazgos evidencian un avance significativo hacia experiencias XR más empáticas, receptivas y centradas en el usuario. No obstante, también revelan desafíos importantes, como la fragmentación tecnológica, la escasa integración entre capas adaptativas y la limitada validación empírica en contextos abiertos. Este estudio ofrece una síntesis crítica de las tendencias emergentes y propone lineamientos estratégicos para diseñar entornos XR éticos, sensibles al contexto y potenciados por inteligencia artificial, convirtiéndose en una guía útil para investigadores, diseñadores y desarrolladores de experiencias inmersivas.

Citas

Berrio, A. C. and Perez, S. J. (2002). Towards a new concept on engineering education. Journal of Educational Technology, Vol. 24, No. 12, pp. 269-286.

Berrio, A. C. and Perez, S. J. (2002). Towards a new concept on engineering education. Journal of Educational Technology, Vol. 24, No. 12, pp. 269-286

Alowayyed, M., Heuvelink, A., & Neerincx, M. (2024). Lilobot: A cognitive conversational agent to train counsellors at children’s helplines. In BNAIC/BeneLearn 2023. https://bnaic2023.tudelft.nl

Araujo, C. H., Aguirre, J. A., & Puig, L. (2024). 3D printing applications and extended realities in medicine: Systematic review. 3D Printing and Additive Manufacturing. https://doi.org/10.1089/3dp.2024.0128

Barsom, E. Z., Graafland, M., & Schijven, M. P. (2020). Systematic review on the effectiveness of augmented reality applications in medical training. Journal of Surgical Education, 77(6), 1650–1664. https://doi.org/10.1016/j.jsurg.2020.05.005

Bozkir, E., Buldu, K. B., Özdel, S., Lau, K. H. C., Wang, M., Saad, D., Schönborn, S., Boch, A., & Kasneci, E. (2024). CUIfy the XR: An open-source package to embed LLM-powered conversational agents in XR. arXiv preprint arXiv:2411.04671. https://doi.org/10.48550/arxiv.2411.04671

Bowman, D. A., Kruijff, E., LaViola, J. J., & Poupyrev, I. (2004). 3D user interfaces: Theory and practice. Addison-Wesley Professional.

Chen, X., Xu, W., Wang, L., & Wang, L. (2021). Adaptive guidance in virtual reality surgical training: A study on user performance and cognitive load. Journal of Biomedical Informatics, 115, 103682. https://doi.org/10.1016/j.jbi.2021.103682

Coban, A., Dzsotjan, D., Küchemann, S., Durst, J., Kuhn, J., & Hoyer, C. (2024). AI support meets AR visualization for Alice and Bob: Personalized learning based on individual ChatGPT feedback in an AR quantum cryptography experiment for physics lab courses. EPJ Quantum Technology, 11(1). https://doi.org/10.1140/epjqt/s40507-025-00310-z

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.

D’Mello, S., Kappas, A., & Gratch, J. (2017). The affective computing approach to affect measurement. Emotion Review, 10(1), 1–10. https://doi.org/10.1177/1754073917696583

Dey, A. K., & Abowd, G. D. (2000). Towards a better understanding of context and context-awareness. In Proceedings of the 2000 Conference on Human Factors in Computing Systems (CHI 2000).

Evangelista Belo, P., Brondi, R., Ferreira, C. A., & Andrade, A. M. (2022). Adaptive multimodal interfaces in XR environments: Toward emotionally aware design. Multimedia Tools and Applications, 81, 19873–19897.

Faiola, A., & Smyslova, O. (2009). Flow experience in Second Life: The impact of telepresence on human-computer interaction. In Online Communities and Social Computing. https://doi.org/10.1007/978-3-642-02774-1_62

Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1

Gehrke, H., Schulte, F. P., & Müller, B. C. N. (2025). Empathic responsiveness in adaptive XR systems: A systematic review. International Journal of Human-Computer Interaction. (en prensa)

Hassenzahl, M. (2010). Experience design: Technology for all the right reasons. Morgan & Claypool. https://doi.org/10.2200/S00261ED1V01Y201003HCI008

Hirzle, T., Bork, F., Daiber, F., & Pfeiffer, T. (2023). The convergence of AI and XR: A review of adaptive immersive systems. Computers & Graphics, 113, 29–48. https://doi.org/10.1016/j.cag.2023.03.006

Isbister, K., & Doyle, P. (2002). Design and evaluation of embodied conversational agents. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 434–441). ACM.

Kleiber, D. A. (2022). Mihaly Csikszentmihalyi: A galvanizing force for the study of experience in the context of leisure. Journal of Leisure Research, 54(2), 175–178. https://doi.org/10.1080/00222216.2021.2022416

Krokos, E., Baker, C., & Varshney, A. (2023). Adaptive scaffolding in virtual reality medical training. IEEE Transactions on Visualization and Computer Graphics, 29(8), 3721–3735. https://doi.org/10.1109/TVCG.2022.3209478

Lan, Y. J. (2020). Immersive, task-based language learning through XR and AI: From design to implementation. TechTrends, 64(6), 1–10. https://doi.org/10.1007/s11528-025-01048-2

Lee, J., Jeong, H., & Rhee, J. (2020). Personalized learning in immersive environments: Adaptive support in VR-based science learning. Educational Technology Research and Development, 68(3), 1689–1712. https://doi.org/10.1007/s11423-019-09721-2

Lohse, K. R., Maslovat, D., & Hodges, N. J. (2022). Cognitive load and retention in immersive virtual environments: A critical review. Computers in Human Behavior, 135, 107342. https://doi.org/10.1016/j.chb.2022.107342

Maniatis, A., Bourou, S., & Anastasakis, Z. (2023). VOXReality: Immersive XR experiences combining language and vision AI models. In AHFE International. https://doi.org/10.54941/ahfe1002938

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

Mourtzis, D., Zogopoulos, V., & Vlachou, E. (2016). Augmented reality application to support the assembly of highly customized products and to adapt to production re-scheduling. International Journal of Advanced Manufacturing Technology, 85(1–4), 389–400. https://doi.org/10.1007/s00170-015-7973-6

Nacke, L. E., Drachen, A., & Göbel, S. (2019). Flow and immersion in video games: The aftermath of a conceptual challenge. Frontiers in Psychology, 10, 1689. https://doi.org/10.3389/fpsyg.2019.01689

Navarro, J., García, A., & Fernández, C. (2023). Dynamic visual aids in mixed reality assembly tasks: Balancing autonomy and guidance. International Journal of Human-Computer Studies, 179, 103120. https://doi.org/10.1016/j.ijhcs.2023.103120

Park, M., Ko, J., Kim, S., & Rho, H. M. (2022). Designing inclusive adaptive XR systems: A user-centered framework for cognitive diversity. Personal and Ubiquitous Computing, 26, 435–450. https://doi.org/10.1007/s00779-021-01549-2

Patel, S. N., Truong, K. N., & Abowd, G. D. (2006). PowerLine Positioning: A practical sub-room-level indoor location system for domestic use. In Proceedings of the 8th International Conference on Ubiquitous Computing.

Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, 103778. https://doi.org/10.1016/j.compedu.2019.103778

Rupp, M. A., Kozachuk, J., & Salas, E. (2019). Adaptive virtual reality training systems for flow preservation. IEEE Transactions on Visualization and Computer Graphics, 25(5), 2041–2049. https://doi.org/10.1109/TVCG.2019.2898736

Sharma, N., Wang, Y., & Liu, Y. (2021). Adaptive narrative delivery in educational XR environments using natural language models. Journal of Educational Technology Systems, 50(1), 97–115.

Slater, M., Spanlang, B., & Corominas, D. (2020). Simulating virtual environments within virtual environments. ACM Transactions on Graphics, 39(4), 1–12. https://doi.org/10.1145/3386569.3392412

Somarathna, D., Sandanayaka, H., Adikari, S., & Wickramasinghe, A. (2023). Exploring user engagement in immersive virtual reality games through multimodal body movements. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581434

Weibel, D., Wissmath, B., & Mast, F. W. (2020). The role of challenge in the flow experience of virtual reality games. Computers in Human Behavior, 112, 106464. https://doi.org/10.1016/j.chb.2020.106464

Yao, J., Zeng, L., Xie, H., & Wang, W. (2024). StepIdeator: Utilizing mixed representations to support step-by-step design with generative artificial intelligence. Journal of Mechanical Design, 147(7), 071703. https://doi.org/10.1115/1.4067429

Zhang, H., Chen, J., & Lee, C. (2022). Virtual patients with generative AI: Conversational agents for immersive clinical training. Simulation in Healthcare, 17(2), 102–110.

Zhou, L., Liu, F., & Tang, Y. (2025). When algorithms meet emotions: Understanding consumer satisfaction in AI companion applications. Journal of Retailing and Consumer Services, 78, 102077. https://doi.org/10.1016/j.jretconser.2025.102077

Zhou, Y., Li, B., Wang, J., & Liu, Y. (2022). Intelligent interaction in XR: Integrating machine learning for adaptive experience design. Multimedia Tools and Applications, 81, 17123–17144. https://doi.org/10.1007/s11042-021-11768-z

Cómo citar

[1]
C. Araujo Mejía, L. Mejía Puig, y D. Gómez Marín, «Inteligencia artificial adaptativa para optimizar experiencias inmersivas en realidades extendidas XR», EIEI ACOFI, sep. 2025.

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Publicado

08-09-2025

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