Diseño y accesibilidad en PlanTEA-WM: una plataforma con IA para planificar rutinas para personas con TEA

Autores/as

DOI:

https://doi.org/10.65234/interaccion.128

Palabras clave:

Tecnologías asistivas, Trastorno del Espectro Autista (TEA), Planificación y anticipación, Pictogramas, Inteligencia Artificial, Diseño Centrado en el Usuario, Usabilidad, Accesibiidad

Resumen

PlanTEA-WM es una plataforma web colaborativa que permite planificar y anticipar rutinas para personas con Trastorno del Espectro Autista (TEA) mediante pictogramas. Surge como evolución de la aplicación móvil PlanTEA, superando sus limitaciones de portabilidad y la ausencia de capacidades multiusuario. El sistema soporta dos roles principales: planificador y planificado, lo que permite la gestión compartida de rutinas en contextos familiares, educativos y clínicos. La plataforma integra funcionalidades clave, como un calendario de eventos, importación y exportación de planificaciones, un buscador de pictogramas, así como un traductor de texto-a-pictogramas apoyado por modelos de Inteligencia Artificial (IA) generativa. Desde sus primeras fases, el diseño ha estado guiado por principios de accesibilidad (WCAG 2.2) y pautas de diseño específicas para usuarios con TEA, validadas mediante un proceso iterativo con asociaciones de personas con TEA y familiares, y personal experto. Estas decisiones han garantizado la literalidad, la reducción de la carga cognitiva y la flexibilidad en los modos de visualización e interacción soportados. Los resultados obtenidos han dado lugar a un sistema robusto y usable, que facilita la anticipación y la colaboración entre diferentes agentes implicados en su uso (personas con TEA, cuidadores y familiares). Como trabajo futuro, se plantea la incorporación de mecanismos de gestión de imprevistos, una IA más adaptativa y su generalización a otros colectivos de usuarios con necesidades de anticipación, planificación y apoyo visual estructurado.

Referencias

Afif, I. Y., Manik, A. R., Munthe, K., Maula, M. I., Ammarullah, M. I., Jamari, J., & Winarni, T. I. (2022). Physiological effect of deep pressure in reducing anxiety of children with ASD during traveling: A public transportation setting. Bioengineering, 9(4), 157. https://doi.org/10.3390/bioengineering9040157 DOI: https://doi.org/10.3390/bioengineering9040157

Aguiar, Y. P., Galy, E., Godde, A., Trémaud, M., & Tardif, C. (2020). AutismGuide: A usability guidelines to design software solutions for users with autism spectrum disorder. Behaviour & Information Technology, 41(11), 1132–1150. https://doi.org/10.1080/0144929X.2020.1856927 DOI: https://doi.org/10.1080/0144929X.2020.1856927

Bjarnason, E., Lang, F., & Mjöberg, A. (2023). An empirically based model of software prototyping: A mapping study and a multi-case study. Empirical Software Engineering, 28(115), 1–40. https://doi.org/10.1007/s10664-023-10331-w DOI: https://doi.org/10.1007/s10664-023-10331-w

Cao, C. C., Ding, Z., Lin, J., & Hopfgartner, F. (2023). AI chatbots as multi-role pedagogical agents: Transforming engagement in CS education. arXiv preprint arXiv:2308.03992. https://arxiv.org/abs/2308.03992

Chen, B., Zhang, Z., Langrené, N., & Zhu, S. (2025). Unleashing the potential of prompt engineering for large language models. Patterns (New York, N.Y.), 6(6), 101260. https://doi.org/10.1016/j.patter.2025.101260 DOI: https://doi.org/10.1016/j.patter.2025.101260

Chia, G. L. C., Anderson, A., & McLean, L. A. (2018). Use of technology to support self-management in individuals with autism: Systematic review. Review Journal of Autism and Developmental Disorders, 5(2), 142–155. https://doi.org/10.1007/s40489-018-0129-5 DOI: https://doi.org/10.1007/s40489-018-0129-5

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008

Fielding, R. T. (2000). Architectural styles and the design of network-based software architectures (Doctoral dissertation, University of California, Irvine). University of California, Irvine. https://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm

Gheewala, S., Xu, S. & Yeom, S. In-depth survey: deep learning in recommender systems—exploring prediction and ranking models, datasets, feature analysis, and emerging trends. Neural Comput & Applic 37, 10875–10947 (2025). https://doi.org/10.1007/s00521-024-10866-z DOI: https://doi.org/10.1007/s00521-024-10866-z

Google DeepMind. (2025). Gemma 3 technical report. arXiv. https://doi.org/10.48550/arXiv.2503.19786

Hartley, C., & Allen, M. L. (2015). Symbolic understanding of pictures in low-functioning children with autism: the effects of iconicity and naming. Journal of autism and developmental disorders, 45(1), 15–30. https://doi.org/10.1007/s10803-013-2007-4 DOI: https://doi.org/10.1007/s10803-013-2007-4

Hernández, P., Molina, A. I., Lacave, C., Rusu, C., & Toledano-González, A. (2022). PlanTEA: Supporting planning and anticipation for children with ASD attending medical appointments. Applied Sciences, 12(10), 5237. https://doi.org/10.3390/app12105237 DOI: https://doi.org/10.3390/app12105237

Hervás, R., Francisco, V., Méndez, G., & Bautista, S. (2019). A user-centred methodology for the development of computer-based assistive technologies for individuals with autism. In D. Lamas, F. Loizides, L. Nacke, H. Petrie, M. Winckler, & P. Zaphiris (Eds.), Human-Computer Interaction – INTERACT 2019 (Lecture Notes in Computer Science, vol. 11746, pp. 75–84). Springer. https://doi.org/10.1007/978-3-030-29381-9_6 DOI: https://doi.org/10.1007/978-3-030-29381-9_6

Jurafsky, D., & Martin, J. H. (n.d.). Speech and language processing (3rd ed., draft). Stanford University. Retrieved September 25, 2025, from https://web.stanford.edu/~jurafsky/slp3/

Lara, J., Lacave, C., & Molina, A. I. (2025). PlanTEA-WM: A Multi-User Web Platform for Routine Planning and Anticipating Everyday Situations in Individuals With Autism Spectrum Disorder. IEEE Access, 13, 180523-180538. https://doi.org/10.1109/access.2025.3617152 DOI: https://doi.org/10.1109/ACCESS.2025.3617152

Logan, K., Iacono, T., & Trembath, D. (2017). A systematic review of research into aided AAC to increase social-communication functions in children with autism spectrum disorder. Augmentative and Alternative Communication, 33(1), 51–64. https://doi.org/10.1080/07434618.2016.1267795 DOI: https://doi.org/10.1080/07434618.2016.1267795

Lord, C., Elsabbagh, M., Baird, G., & Veenstra-Vanderweele, J. (2018). Autism spectrum disorder. The Lancet, 392(10146), 508–520. https://doi.org/10.1016/S0140-6736(18)31129-2 DOI: https://doi.org/10.1016/S0140-6736(18)31129-2

Morales-Hidalgo, P., Roigé-Castellví, J., Hernández-Martínez, C., Voltas, N., & Canals, J. (2018). Prevalence and characteristics of autism spectrum disorder among Spanish school-age children. Journal of Autism and Developmental Disorders, 48(10), 3176–3190. https://doi.org/10.1007/s10803-018-3581-2 DOI: https://doi.org/10.1007/s10803-018-3581-2

Neimy, H., & Fossett, B. (2022). Augmentative and Alternative Communication (AAC) systems. In Handbook of special education research (pp. 375–401). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-96478-8_20 DOI: https://doi.org/10.1007/978-3-030-96478-8_20

Rajbhandari, S., Li, C., Yao, Z., Zhang, M., Aminabadi, R. Y., Awan, A. A., Rasley, J., & He, Y. (2022). DeepSpeed-MoE: Advancing Mixture-of-Experts inference and training to power next-generation AI scale. arXiv. https://doi.org/10.48550/arXiv.2201.05596

Rydzewska, E. (2016). Unexpected changes of itinerary – adaptive functioning difficulties in daily transitions for adults with autism spectrum disorder. European Journal of Special Needs Education, 31(3), 330–343. https://doi.org/10.1080/08856257.2016.1187889 DOI: https://doi.org/10.1080/08856257.2016.1187889

Schuck, R. K., & Fung, L. K. (2024). A dual design thinking–universal design approach to catalyze neurodiversity advocacy through collaboration among high-schoolers. Frontiers in Psychiatry, 14, 1250895. https://doi.org/10.3389/fpsyt.2023.1250895 DOI: https://doi.org/10.3389/fpsyt.2023.1250895

Singh, R., & Gill, S. S. (2023). Edge AI: A survey. Internet of Things and Cyber-Physical Systems, 3, 71-92. https://doi.org/10.1016/j.iotcps.2023.02.004 DOI: https://doi.org/10.1016/j.iotcps.2023.02.004

Tam, T. Y. C., et al. (2024). A framework for human evaluation of large language models. NPJ Digital Medicine, 7(1), 58. https://doi.org/10.1038/s41746-024-01258-7 DOI: https://doi.org/10.1038/s41746-024-01258-7

Uitdenbogerd, A. L., Spichkova, M., & Alzahrani, M. (2022). Web-based search: How do animated user interface elements affect autistic and non-autistic users? arXiv. https://arxiv.org/abs/2211.11993 DOI: https://doi.org/10.5220/0011074500003176

Valencia, K., Rusu, C., Quiñones, D., & Jamet, E. (2019). The Impact of Technology on People with Autism Spectrum Disorder: A Systematic Literature Review. Sensors (Basel, Switzerland), 19(20), 4485. https://doi.org/10.3390/s19204485 DOI: https://doi.org/10.3390/s19204485

Valencia, K., Rusu, C., & Botella, F. (2021). User Experience Factors for People with Autism Spectrum Disorder. Applied Sciences, 11(21), 10469. https://doi.org/10.3390/app112110469 DOI: https://doi.org/10.3390/app112110469

Valencia, K., Botella, F., & Rusu, C. (2022). A property checklist to evaluate the user experience for people with autism spectrum disorder. In G. Z. Yang (Ed.), Social computing and social media: Design, user experience and impact. SCSM 2022. Lecture Notes in Computer Science (Vol. 13335, pp. 205–216). Springer. https://doi.org/10.1007/978-3-031-05061-9_15 DOI: https://doi.org/10.1007/978-3-031-05061-9_15

Valencia, K., Hernández del Mazo, P., Molina, A. I., Lacave, C., Rusu, C., & Botella, F. (2024). Evaluating PlanTEA: The practice of a UX evaluation methodology for people with ASD. Universal Access in the Information Society. Advance online publication. https://doi.org/10.1007/s10209-024-01175-2 DOI: https://doi.org/10.1007/s10209-024-01175-2

Villamin, G. R., & Luppicini, R. (2024). Co-Designing Digital Assistive Technologies for Autism Spectrum Disorder (ASD) Using Qualitative Approaches. International Journal of Disability, Development and Education, 1–19. https://doi.org/10.1080/1034912X.2024.2427606 DOI: https://doi.org/10.1080/1034912X.2024.2427606

Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2022). Finetuned language models are zero-shot learners. arXiv. https://doi.org/10.48550/arXiv.2109.01652

World Wide Web Consortium (W3C). (2023). Web Content Accessibility Guidelines (WCAG) 2.2. W3C Recommendation. https://www.w3.org/TR/WCAG22/

Zhang, B., Qi, Y., Yang, Y., & Zhang, J. (2025). Research on the interface design of ASD children intervention app based on Kano-entropy weight method. Frontiers in Psychiatry, 16, 1508006. https://doi.org/10.3389/fpsyt.2025.1508006 DOI: https://doi.org/10.3389/fpsyt.2025.1508006

Descargas

Publicado

2025-12-23