Supporting Marginalized Learners with GenAI (Extended Abstract)

Abstract

Access to quality education remains deeply unequal, particularly for learners in low-resource and socio-politically unstable contexts. Although Generative AI (GenAI) interventions have the potential to provide educational support, they are rarely designed and developed with marginalized communities, especially within challenging socio-political environments. My research addresses this gap by exploring, designing, and developing responsive GenAI interventions specifically tailored to support women in Afghanistan pursuing programming education despite substantial barriers. Through exploratory research, participatory design, and iterative evaluation, I aim to uncover how GenAI can effectively enhance learning outcomes and aspirations within this underserved community. Ultimately, my work contributes to more equitable AI development practices and actively supports marginalized communities by enabling access to meaningful education and the development of employable skills.