Architectural Proposal for a Hybrid Recommender Engine for Accessible Educational Content
DOI:
https://doi.org/10.67294/ayj6b114Keywords:
Inclusive Recommender System, Digital Accessibility, Visual Impairment, Artificial Intelligence, Accessible Educational ContentAbstract
This paper presents an architectural proposal for a modular and adaptive hybrid recommender system designed to improve equitable access to PDF-based educational content for students with visual impairments. The system overcomes the limitations of traditional recommenders, which optimize rankings solely by thematic relevance while ignoring format-related barriers, by implementing a closed-loop approach that prioritizes technical accessibility prior to ranking the educational material. The architecture is structured into four independent layers: automated ingestion via multimodal artificial intelligence, dynamic student profiling, a sequential hybrid recommendation engine, and a semantic presentation interface. The technical viability of the proposal was successfully validated through a functional Python prototype integrated with the Gemini 2.5 Flash model from Google AI Studio, evaluating a repository of real-world documents split between the fields of algebra and programming. Experimental results demonstrated that the system accurately resolves the cold-start problem through an initial technical questionnaire and effectively mitigates the occurrence of false positives in practice, thanks to a reverse feedback loop that automatically updates functional security profiles and restricts resources reported with access flaws.
References
Acosta-Vargas, P., Luján-Mora, S., Acosta, T., & Salvador-Ullauri, L. (2017). Accesibilidad de documentos PDF en repositorios educativos de Latinoamérica. Enfoque UTE, 8(1), 162–177. https://dialnet.unirioja.es/servlet/articulo?codigo=10207290
Aljedaani, W., Rudhravaram, S. K., Chintham, A., Habib, A., & Eler, M. M. (2024). A11yPDF: Bridging the gap to inclusive PDFs. Proceedings of the 21st International Web for All Conference, 37–38. https://doi.org/10.1145/3677846.3677859
Burke, R. (2017). Multisided fairness for recommendation. In Proceedings of the Workshop on Fairness, Accountability and Transparency in Machine Learning. https://doi.org/10.48550/arXiv.1707.00093
Deng, L., Rattadilok, P., & Xiong, R. (2021). A machine learning-based monitoring system for attention and stress detection for children with autism spectrum disorders. Proceedings of the 3rd International Conference on Intelligent Medicine and Health, 1–5. https://doi.org/10.1145/3484377.3484381
Drigas, A., & Ioannidou, R. E. (2013). Special Education and ICTs. International Journal of Emerging Technologies in Learning (iJET), 8(2), 41–47. https://doi.org/10.3991/ijet.v8i2.2514
Ekstrand, M. D., Burke, R., & Diaz, F. (2019). Fairness and discrimination in retrieval and recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’19) (pp. 1403–1404). Association for Computing Machinery. https://doi.org/10.1145/3331184.3331380
ISO. (2014). Document management applications — Electronic document file format enhancement for accessibility — Part 1: Use of ISO 32000-1 (PDF/UA-1) (ISO Standard No. 14289-1:2014). International Organization for Standardization. https://www.iso.org/standard/64599.html
Jiménez Martínez, L. D. (2025). Diseño y desarrollo de una plataforma web de aprendizaje en línea integrando aula invertida y adaptada para usuarios débiles visuales [Tesis de maestría, Universidad Autónoma de Aguascalientes]. Repositorio Institucional UAA. http://bdigital.dgse.uaa.mx:8080/xmlui/handle/11317/3289
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4–5), 441–504. https://doi.org/10.1007/s11257-011-9118-4
Kumar, A., Padath, T., & Wang, L. L. (2025). Benchmarking PDF accessibility evaluation: A dataset and framework for assessing automated and LLM-based approaches for accessibility testing. ASSETS '25: Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility, 1–24. https://doi.org/10.1145/3663547.3746380
Kumar, A., & Wang, L. L. (2024). Uncovering the new accessibility crisis in scholarly PDFs: Publishing model and platform changes contribute to declining scholarly document accessibility in the last decade. Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility, 1–16. https://doi.org/10.1145/3663548.3675634
Loitsch, C., Weber, G., Kaklanis, N., Votis, K., & Tzovaras, D. (2017). A knowledge-based approach to user interface adaptation from preferences and for special needs. User Modeling and User-Adapted Interaction, 27(3-5), 1–20. https://doi.org/10.1007/s11257-017-9196-z
Mauro, N., Ardissono, L., & Cena, F. (2020). Personalized recommendation of PoIs to people with autism. Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 1–8. https://doi.org/10.1145/3340631.3394845
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
Naghiaei, M., et al. (2022). CPFair: Personalized consumer and producer fairness re-ranking for recommender systems. arXiv. https://doi.org/10.48550/arXiv.2204.08085
Namoune, A., Sen, A. A., Tufail, A., Alshanqiti, A. M., Nawaz, W., & Benrhouma, O. (2022). A two-phase machine learning framework for context-aware service selection to empower people with disabilities. Sensors, 22(14), Article 5142. https://doi.org/10.3390/s22145142
Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web (pp. 325–341). Springer. https://doi.org/10.1007/978-3-540-72079-9_10
Santos, F., de Almeida, A., Martins, C., Gonçalves, R., & Martins, J. (2017). Using POI functionality and accessibility levels for delivering personalized tourism recommendations. Computers, Environment and Urban Systems, 77, 1–15. https://doi.org/10.1016/j.compenvurbsys.2017.08.007
Salehie, M., & Tahvildari, L. (2009). Self-adaptive software: Landscape and research challenges. ACM Transactions on Autonomous and Adaptive Systems, 4(2), Article 14, 1–42. https://doi.org/10.1145/1516533.1516538
Seale, J. (2014). E-learning and disability in higher education: Accessibility research and practice (2nd ed.). Routledge. https://www.routledge.com/E-learning-and-Disability-in-Higher-Education-Accessibility-Research-and-Practice/Seale/p/book/9780415629416
Tarus, J. K., Niu, Z., & Mustafa, G. (2018). Knowledge-based recommendation: A review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 50(1), 21–48. https://doi.org/10.1007/s10462-017-9539-5
Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnić, I., & Duval, E. (2012). Context-aware recommender systems for learning: A survey and future challenges. IEEE Transactions on Learning Technologies, 5(4), 318–335. https://doi.org/10.1109/TLT.2012.11
Vigo, M., Brown, J., & Conway, V. (2013). Benchmarking web accessibility evaluation tools: Measuring the harm of sole reliance on automated tests. In Proceedings of the International Cross-Disciplinary Conference on Web Accessibility (W4A ’13). Association for Computing Machinery. https://doi.org/10.1145/2461121.2461124
W3C. (2024). WCAG 2 overview. World Wide Web Consortium. https://www.w3.org/WAI/standards-guidelines/wcag/
Zulfiqar, S., Arooj, S., Umar Hayat, S., Shahid, S., & Karim, A. (2020). Automated generation of accessible PDF. Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility, 1–3. https://doi.org/10.1145/3373625.3418045
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Luis David Jiménez Martínez, Francisco J. Álvarez Rodríguez (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
