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This paper explores how artificial intelligence (AI) technologies in education reproduce systemic inequalities by embedding ableist and normative assumptions into their design and use. Drawing on frameworks from Critical Disability Studies, disability justice, and intersectionality, we develop a theoretical approach that interrogates the sociotechnical processes through which AI marginalizes non-normative ways of learning and knowing. Instead of viewing AI harms as inevitable or merely technical, we examine how these systems are influenced by policy, ideology, and sociocultural context. We connect technosolutionist logics in education to broader narratives of artificial general intelligence, showing how both obscure the contributions of disabled people and other multiply marginalized communities. Through theoretically grounded case analyses of AI tools such as chatbots, learning analytics, robotic tutors, and intelligent tutoring systems, we highlight patterns of sociotechnical ableism, algorithmic marginalization, and limited technological agency. The paper concludes by advocating for participatory, justice-oriented approaches to AI in education that center human variation and resist the erasure of disabled experience.
Foley et al. (Thu,) studied this question.