Investigating Instructors’ Experiences in a Neurodiversity-Focused AI Training Program

Introduction Artificial Intelligence (AI) has become a cornerstone of technological advancement, shaping industries from healthcare to manufacturing. As the global reliance on AI grows, so does the need for a workforce capable of navigating its complexities. Central to this workforce is the imperative to include diverse perspectives, which drive innovation and ensure the equitable application of AI technologies. Fostering such diversity requires addressing existing educational access and inclusivity disparities, particularly for neurodivergent learners who remain underrepresented in STEM fields, including AI education. Despite the increasing focus on equity and inclusion within engineering and computer science education, neurodivergent learners often encounter systemic barriers that limit their engagement and success. Traditional pedagogical models frequently overlook the varied learning preferences and social dynamics associated with neurodiversity. For example, instructional approaches prioritizing uniformity in cognitive or social engagement may inadvertently disadvantage students whose strengths lie outside conventional frameworks. To build a genuinely inclusive AI education ecosystem, educators must recognize these challenges and adapt their teaching strategies to meet the unique needs of neurodivergent learners. This study investigates instructors’ experiences in a neurodiversity-focused AI training program to bridge critical gaps in understanding how educators navigate these complexities. Specifically, the research explores the challenges instructors face when teaching AI concepts, including balancing diverse learning styles, managing classroom dynamics, and implementing effective assessment strategies. These insights can guide the development of educational practices that are both inclusive and effective, enabling more students to contribute meaningfully to the AI workforce.