ALGORITHMIC ABLEISM IN EDUCATION: RISKS AND CHALLENGES OF GENERATIVE ARTIFICIAL INTELLIGENCE FOR INCLUSIVE PEDAGOGICAL PRACTICES
DOI:
https://doi.org/10.63330/aurumpub.067-101Keywords:
Ableism, Algorithmic Bias, Generative Artificial Intelligence, Inclusive Education, People with DisabilitiesAbstract
The expansion of Generative Artificial Intelligence (GenAI) in the educational field has broadened the possibilities for producing instructional materials, personalizing learning, designing assessments, and supporting pedagogical practices. However, generative systems are developed from large datasets and may reproduce biases, stereotypes, and socially embedded patterns present in such content, raising specific concerns within the context of inclusive education. In this regard, this study aims to analyze, based on the literature, the risks of reproducing ableist conceptions associated with the use of Generative Artificial Intelligence in educational practices. The term algorithmic ableism is adopted as an analytical category to examine situations in which AI systems may produce, reinforce, or normalize representations, classifications, and recommendations grounded in normative conceptions of disability and ability. Methodologically, this is a bibliographic study with a qualitative approach and an exploratory-descriptive nature, grounded in scientific publications and institutional documents related to GenAI, algorithmic bias, ableism, disability, and inclusive education. The discussion focuses on the risks associated with the generation of instructional materials, educational responses, assessment processes, and AI-mediated pedagogical recommendations. It is argued that the uncritical use of these tools may reproduce stereotypes, generalize individual needs, reinforce lowered expectations regarding learning, and overlook the diversity among students with disabilities. Conversely, the responsible use of GenAI, combined with human oversight, accessibility, teacher education, critical evaluation, and the participation of people with disabilities in the development and governance of these technologies, may contribute to more inclusive educational practices. It is concluded that the incorporation of GenAI into inclusive education requires ongoing mechanisms for identifying and mitigating biases, so that technological innovation does not digitally reproduce barriers and ableist conceptions already present in social and educational structures.
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