Exploratory mixed-methods study examines AI's role in promoting educational equity for neurodivergent students in Moroccan universities, suggesting systemic reforms are necessary.
Neurodivergent students, particularly those with Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and DYS-related learning differences, remain largely overlooked within Moroccan higher education. Standardized pedagogical models, combined with the massification of universities, continue to limit equitable participation, leaving many students undiagnosed and insufficiently supported. At the same time, the growing availability of Artificial Intelligence (AI) and assistive technologies introduces new possibilities for more adaptive and inclusive learning environments. This study explores how Moroccan universities currently engage with neurodivergence and examines instructors’ perceptions of the potential role of AI-based and assistive tools in promoting educational equity. Drawing on an exploratory mixed-methods survey administered to instructors across multiple disciplines and institutions, the findings reveal strong normative support for inclusion alongside persistent structural gaps. These include limited formal training on neurodiversity, the absence of institutional guidelines, uneven access to assistive technologies, and pedagogical constraints linked to overcrowded classrooms. The analysis further highlights the ethical and governance challenges associated with deploying AI in under-regulated contexts, particularly concerning data privacy, algorithmic bias, and the risk of techno-solutionism. The paper argues that technological innovation alone cannot ensure inclusion. Rather, meaningful and sustainable change requires coordinated institutional reform, faculty capacity building, infrastructural investment, and a shift toward equity-oriented pedagogical cultures. Integrating AI within such a broader framework is essential for advancing inclusive higher education in Morocco and comparable Global South contexts.
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ASSABBANE et al. (2025) studied this question.
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