Publicação
AI-driven solutions for enhancing digital accessibility in higher education
| datacite.subject.fos | Ciências Sociais | |
| datacite.subject.sdg | 04:Educação de Qualidade | |
| dc.contributor.author | Costa, José Paulo | |
| dc.contributor.author | Nacheva, Radka | |
| dc.contributor.author | Coelho, Ana Sofia | |
| dc.contributor.author | Martins, Oliva M.D. | |
| dc.date.accessioned | 2026-03-05T10:33:16Z | |
| dc.date.available | 2026-03-05T10:33:16Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Digital accessibility is essential for ensuring that students with disabilities have equal access to educational materials in higher education. Despite the existence of 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 fall short in automating the remediation process or offering personalized adjustments for individual learners. This paper presents an AI-driven framework designed to automate the detection, remediation, and personalization of digital content 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 successfully remediates 85% of identified issues. Additionally, the framework offers real-time personalized 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. | por |
| dc.identifier.citation | Costa, J. P.; Nacheva, R.; Coelho, A. S.; Martins, O.M. D. (2025). AI-driven solutions for enhancing digital accessibility in higher education. In 59th AMSTERDAM International Congress on Research in Science, Technology, Social Sciences & Education (STSSE-25). DOI: 10.17758/0325104 | |
| dc.identifier.doi | 10.17758/0325104 | |
| dc.identifier.uri | http://hdl.handle.net/10198/35956 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.relation.hasversion | https://eares.org/siteadmin/upload/DiR0325104.pdf | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Digital accessibility | |
| dc.subject | Artificial intelligence | |
| dc.subject | Machine learning | |
| dc.subject | Higher education | |
| dc.subject | Inclusive education | |
| dc.subject | Personalized learning | |
| dc.subject | Accessibility compliance | |
| dc.title | AI-driven solutions for enhancing digital accessibility in higher education | eng |
| dc.type | conference paper not in proceedings | |
| dspace.entity.type | Publication | |
| oaire.citation.conferenceDate | 2025 | |
| oaire.citation.title | 59th AMSTERDAM International Congress on Research in Science, Technology, Social Sciences & Education (STSSE-25) | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Coelho | |
| person.familyName | Martins | |
| person.givenName | Ana Sofia | |
| person.givenName | Oliva M.D. | |
| person.identifier | 2270676 | |
| person.identifier | 1025091 | |
| person.identifier.ciencia-id | BC1C-630F-3EA4 | |
| person.identifier.ciencia-id | 221F-FF93-8879 | |
| person.identifier.orcid | 0000-0003-3389-3231 | |
| person.identifier.orcid | 0000-0002-2958-691X | |
| person.identifier.rid | J-5951-2015 | |
| person.identifier.scopus-author-id | 55324743500 | |
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| relation.isAuthorOfPublication | faaf8b5a-a36d-41ef-89e1-34772e67a535 | |
| relation.isAuthorOfPublication.latestForDiscovery | 111469c0-b9b7-4769-ba84-5e501efb9534 |