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Sustainable AI Solutions for Empowering Visually Impaired Students: The Role of Assistive Technologies in Academic Success

Author

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  • Ibrahim A. Elshaer

    (Department of Management, School of Business, King Faisal University, Al-Ahsaa 31982, Saudi Arabia
    King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia)

  • Sameer M. AlNajdi

    (King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
    Education Technology Department, Faculty of Education and Arts, University of Tabuk, Tabuk 71491, Saudi Arabia)

  • Mostafa A. Salem

    (King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
    Deanship of Development and Quality Assurance, King Faisal University, Al-Ahsaa 31982, Saudi Arabia)

Abstract

This paper examines the impacts of AI-powered assistive technologies (AIATs) on the academic success of higher education university students with visual impairments. As digital learning contexts become progressively more prevalent in higher education institutions, it is critical to understand how these technologies foster the academic success of university students with blindness or low vision. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study conducted a quantitative research approach and collected data from 390 visually impaired students who were enrolled in different universities across Saudi Arabia (SA). Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the paper tested the influences of four UTAUT dimensions—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)—on Academic Performance (AP), while also evaluating the mediating role of Behavioral Intention (BI). The results revealed a significant positive relationship between the implementation of AI-based assistive tools and students’ academic success. Particularly, BI emerged as a key mediator in these intersections. The results indicated that PE (β = 0.137, R 2 = 0.745), SI (β = 0.070, R 2 = 0.745), and BI (β = 0.792, R 2 = 0.745) significantly affected AP. In contrast, EE (β = −0.041, R 2 = 0.745) and FC (β = −0.004, R 2 = 0.745) did not have a significant effect on AP. Concerning predictors of BI, PE (β = 0.412, R 2 = 0.317), SI (β = 0.462, R 2 = 0.317), and EE (β = 0.139, R 2 = 0.317) were all positively associated with BI. However, FC had a significant negative association with BI (β = −0.194, R 2 = 0.317). Additionally, the analysis revealed that EE, SI, and PE can all indirectly enhance Academic Performance by influencing BI. The findings provide practical insights for higher education policymakers, higher education administrators, and AI designers, emphasizing the need to improve the accessibility and usability of sustainable and long-term assistive technologies to better accommodate learners with visual impairments in higher education contexts.

Suggested Citation

  • Ibrahim A. Elshaer & Sameer M. AlNajdi & Mostafa A. Salem, 2025. "Sustainable AI Solutions for Empowering Visually Impaired Students: The Role of Assistive Technologies in Academic Success," Sustainability, MDPI, vol. 17(12), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5609-:d:1681818
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    References listed on IDEAS

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    4. Mostafa Aboulnour Salem & Wafaa Hassanien Alsyed & Ibrahim A. Elshaer, 2022. "Before and Amid COVID-19 Pandemic, Self-Perception of Digital Skills in Saudi Arabia Higher Education: A Longitudinal Study," IJERPH, MDPI, vol. 19(16), pages 1-13, August.
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    Cited by:

    1. Mostafa Aboulnour Salem, 2025. "A Digital Sustainability Lens: Investigating Medical Students’ Adoption Intentions for AI-Powered NLP Tools in Learning Environments," Sustainability, MDPI, vol. 17(14), pages 1-18, July.
    2. Young Mee Jung & Hyeon Jo, 2025. "Understanding Continuance Intention of Generative AI in Education: An ECM-Based Study for Sustainable Learning Engagement," Sustainability, MDPI, vol. 17(13), pages 1-21, July.

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