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Automating digital accessibility AI and machine learning for inclusive learning environments

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datacite.subject.fosCiências Sociais
datacite.subject.sdg04:Educação de Qualidade
dc.contributor.authorCosta, José Paulo
dc.contributor.authorCoelho, Ana Sofia
dc.contributor.authorMartins, Oliva M.D.
dc.contributor.editorSpringer Nature
dc.date.accessioned2026-03-05T11:47:19Z
dc.date.available2026-03-05T11:47:19Z
dc.date.issued2026
dc.description.abstractDigital accessibility is essential for ensuring that students with disabilities have equal access to educational materials in higher education. Despite standards like the Web Content Accessibility Guidelines (WCAG) and PDF Universal Access (PDF/UA), many institutions still face challenges in providing accessible digital content. Existing tools can identify accessibility issues but often fail to automate the remediation process or offer personalised adjustments for individual learners. This paper presents an AI-driven framework designed to automate digital content detection, remediation, and personalisation to meet accessibility requirements. The proposed framework integrates AI and machine learning to enhance the accessibility of PDFs, HTML content, and multimedia resources, ensuring compliance with WCAG 2.1 and PDF/UA standards. The study demonstrates that the AI system detects accessibility issues with 92% accuracy and remediates 85% of identified problems. Additionally, the framework offers real-time personalised adjustments, improving user satisfaction for 94% of students with disabilities. The AI system also reduces the time and cost of ensuring accessibility, making it an efficient tool for educational institutions. The paper concludes with recommendations for further research to expand the framework’s capabilities and offers insights for developing inclusive education policies that leverage AI technology.eng
dc.identifier.citationCosta, J. P.; Coelho, A. S.; Martins, O.M.D. (2026). Automating digital accessibility AI and machine learning for inclusive learning environments. In 20th Iberian Conference on Information Systems and Technologies (CISTI 2025). CISTI 2025. Springer, Cham. DOI: 10.1007/978-3-032-10731-2_1
dc.identifier.doi10.1007/978-3-032-10731-2_1
dc.identifier.urihttp://hdl.handle.net/10198/35961
dc.language.isoeng
dc.peerreviewedyes
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-032-10731-2_1#citeas
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDigital accessibility
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectHigher education
dc.subjectInclusive education
dc.subjectPersonalized learning
dc.subjectAccessibility compliance
dc.titleAutomating digital accessibility AI and machine learning for inclusive learning environmentseng
dc.typeconference proceedings
dspace.entity.typePublication
oaire.citation.title20th Iberian Conference on Information Systems and Technologies (CISTI 2025)
oaire.citation.volume1718
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCoelho
person.familyNameMartins
person.givenNameAna Sofia
person.givenNameOliva M.D.
person.identifier2270676
person.identifier1025091
person.identifier.ciencia-idBC1C-630F-3EA4
person.identifier.ciencia-id221F-FF93-8879
person.identifier.orcid0000-0003-3389-3231
person.identifier.orcid0000-0002-2958-691X
person.identifier.ridJ-5951-2015
person.identifier.scopus-author-id55324743500
relation.isAuthorOfPublication111469c0-b9b7-4769-ba84-5e501efb9534
relation.isAuthorOfPublicationfaaf8b5a-a36d-41ef-89e1-34772e67a535
relation.isAuthorOfPublication.latestForDiscovery111469c0-b9b7-4769-ba84-5e501efb9534

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