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Título del libro: Advances In Soft Computing, Micai 2025, Pt Ii
Título del capítulo: Machine Learning?Driven Virtual Reality Intervention for Academic Social Anxiety: A Symbolic AI Prototype

Autores UNAM:
JOSE MANUEL SANCHEZ SORDO; SERGIO TEODORO VITE;

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Autores externos:

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Idioma:

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Año de publicación:
2026

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Palabras clave:

Decision-tree model; Immersive virtual reality; Load index; Machine-learning; Performance; Public speaking; Social anxieties; University students; Users' experiences; Virtual-reality environment

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Resumen:

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.Fear of public speaking is one of the most prevalent forms of social anxiety, significantly impairing academic and professional performance. This study presents the development of a prototype virtual reality (VR) treatment supported by artificial intelligence (AI), aimed at reducing social anxiety in academic contexts. A decision tree model was trained using the C4.5 supervised learning algorithm on data from 134 university students. Based on the model?s classifications, immersive VR environments simulating public speaking scenarios were designed and implemented using the Unity engine and C#. User experience was evaluated through the NASA Task Load Index (NASA-TLX) in a sample of 20 psychology students. The model achieved 97% classification accuracy (? = 0.94, F1 = 0.97), indicating high reliability in identifying anxiety levels. Participants reported moderate-to-high cognitive load (M = 6.3 for mental demand and effort) and moderate frustration (M = 6.1). These findings suggest that supervised learning models, such as implementations of the C4.5 algorithm, can effectively analyze and classify cognitive and behavioral patterns associated with academic social anxiety, offering a robust basis for therapeutic design. The resulting VR treatment appears promising for anxiety management, although improvements in interactivity and adaptive features are warranted. Future research should assess its clinical efficacy in larger and more diverse populations.

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Entidades citadas de la UNAM:

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Fuente:



ISBN:
9783032090430
Editorial:
SPRINGER INTERNATIONAL PUBLISHING AG

Tipo de producto:
Conference Paper

DOI:
10.1007/978-3-032-09044-7_29