A Model for the Adoption of Artificial Intelligence in Inclusive Education: An Exploratory Study of Key Factors and Expert Insights

Aim/Purpose: This study adopts a mixed-method approach to examine the factors influencing the adoption of AI-based assistive technologies among students with special needs. Specifically, it explores the roles of social support, motivation, digital literacy, and self-efficacy in shaping students’ perceptions and behavioral intentions toward these technologies within inclusive education settings. Background: The integration of assistive technologies into inclusive learning environments has gained significant attention due to its potential to improve educational outcomes for students with disabilities. Despite this growing interest, limited empirical research has explored the determinants of such technologies’ adoption, particularly through the technology acceptance model lens. This study addresses this gap by extending the technology acceptance model framework to incorporate additional constructs such as social support, motivation, digital literacy, and self-efficacy, offering a more comprehensive understanding of user acceptance in inclusive contexts. Methodology: A mixed-method design was employed. The quantitative phase involved a survey of 118 students enrolled in six inclusive education programs across Saudi Arabian universities. Data were analyzed using structural equation modeling via SmartPLS to examine the relationships between the proposed constructs. Complementing this, the qualitative phase included in-depth interviews with eight AI experts specializing in assistive technologies for students with disabilities, providing contextual insights and validating the quantitative findings. Contribution: This study advances the technology acceptance model by integrating key psychological and social variables (social support, motivation, digital literacy, and self-efficacy) into the adoption framework. Doing so offers a more nuanced perspective on how students with special needs interact with and perceive AI-based assistive tools, addressing a critical gap in current inclusive education research. Findings: The findings from the quantitative analysis indicate that social support and motivation significantly enhance perceived usefulness, while digital literacy and self-efficacy significantly enhance perceived ease of use. Both usefulness and ease of use substantially affect intention toward adopting assistive technology. Additionally, the qualitative findings reveal key themes emphasizing user-friendly designs, contextual adaptability, ethical considerations, and AI’s potential to promote student autonomy. Recommendations for Practitioners: Expert interviews underscore the necessity for ongoing professional development among educators to effectively implement assistive AI tools. Furthermore, fostering partnerships between technology developers and educators is critical for designing tools that meet the real-world needs of inclusive classrooms. Recommendation for Researchers: By extending the technology acceptance model, this study opens new avenues for future research into other influencing factors, such as institutional readiness, cultural considerations, and policy support. Researchers are encouraged to apply this enriched model in various educational and geographical contexts to further validate its applicability. Impact on Society: Understanding the factors that influence the adoption of assistive technology is essential for fostering inclusive and equitable education systems. This study contributes to the development of policies and implementation strategies that ensure students with disabilities can fully benefit from emerging educational technologies. Future Research: Future studies can expand the scope of this research by exploring different educational settings, conducting longitudinal studies to assess long-term adoption trends, and incorporating additional stakeholders, such as parents and policymakers, to gain a more comprehensive understanding of assistive technology integration.