Bridging the Gap: The Role of AI-Driven Assistive Technologies for Visually Impaired Students in Higher Education
Abstract
Background. Higher education institutions increasingly adopt digitally mediated learning environments; however, visually impaired students continue to face persistent structural barriers in accessing complex visual and multimedia academic content. Conventional assistive technologies largely rely on basic text-to-speech functions and remain limited in interpreting diagrams, charts, and discipline-specific visual materials, particularly in STEM-related fields.
Purpose. This study aimed to examine the role of AI-driven assistive technologies in enhancing accessibility, academic performance, and learning autonomy among visually impaired students in higher education contexts.
Method. An explanatory sequential mixed-methods design was employed involving 72 visually impaired students from three universities. Quantitative data were collected through GPA analysis, engagement and learning autonomy scales, and usability measures to compare outcomes between users of AI-based assistive technologies and conventional assistive tools. Qualitative data were obtained through semi-structured interviews to explore learners’ experiences and institutional support mechanisms.
Results. Inferential analysis revealed statistically significant improvements in academic performance, engagement, and perceived learning autonomy among students using AI-driven assistive technologies (p < 0.001). Learning autonomy emerged as a significant predictor of academic success. Qualitative findings indicated improved comprehension of visual academic content and increased classroom participation facilitated by AI-supported tools.
Conclusion. AI-driven assistive technologies function as transformative accessibility solutions when integrated with adequate institutional support, contributing to more equitable academic participation and the development of inclusive higher education ecosystems for visually impaired learners.
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Copyright (c) 2026 Moh Solehuddin, Ahmed Mohamed, Amina Said
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