AI-Enhanced Digital Information Literacy for Inclusive Education: A Conceptual Framework for Learners with Special Educational Needs and Disabilities

Abstract

Artificial intelligence (AI) can expand access, personalisation, communication, and participation in inclusive education, yet it can also reproduce barriers when learners lack the digital information literacy (DIL) needed to judge AI outputs, protect privacy, recognise bias, and use assistive or generative tools with agency. This conceptual review reframes AI-enhanced DIL for learners with special educational needs and disabilities (SEND) by integrating research on AI literacy, special-education technology, Universal Design for Learning (UDL), media and information literacy, and human-centred AI. A critical integrative synthesis identified recurring competencies and implementation conditions across foundational and recent literature. The resulting Inclusive AI-Enhanced DIL framework comprises four interdependent pillars: (1) Accessible Technical Fluency, (2) Critical Information Verification, (3) Ethical and Rights-Based AI Use, and (4) Adaptive, Self-Determined Application, with accessibility and learner agency operating across all four. Its distinctive contribution is to conceptualise AI literacy as equitable information participation rather than generic tool proficiency: learners should be able to access AI-mediated information, judge its credibility, protect their rights, adapt support to individual needs, and retain meaningful control over decisions. The framework connects learner competencies with teacher capability, accessible instructional design, institutional governance, and equitable infrastructure, and proposes performance-based assessment through accessible portfolios, verification tasks, supported reflection, and authentic AI-use scenarios. Empirical validation across disability groups, languages, socioeconomic contexts, and levels of digital access is required.