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

Ibrahim A. Elshaer (), Sameer M. AlNajdi and Mostafa A. Salem
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Ibrahim A. Elshaer: Department of Management, School of Business, King Faisal University, Al-Ahsaa 31982, Saudi Arabia
Sameer M. AlNajdi: King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
Mostafa A. Salem: King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia

Sustainability, 2025, vol. 17, issue 12, 1-18

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.

Keywords: artificial intelligence; sustainability; assistive technology; academic success; visual impairment; university students (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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