Digital 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.