Negotiating the machine: Teacher and student resistance to linguistically unequal AI in Moroccan multilingual education - El Gourari, Wyssal; Ed-Dali, Rachid (American Journal of STEM Education, 2026)
Educational AI deployment in multilingual contexts often assumes passive adoption, yet little research examines how users respond to systems positioning languages unequally. This ethnographic study investigated teacher and student responses to AI systems treating French and Arabic differently in Moroccan secondary schools. Eighteen months of fieldwork across four schools included classroom observations (N=78), interviews (N=15), and student focus groups (N=60). Thematic analysis revealed three patterns: teachers developed systematic appropriation strategies in 67% of lessons (78% in Arabic-medium contexts), students recognized differential treatment (78%) and created tactical workarounds, and collective resistance emerged through professional networks and advocacy. Findings challenge technological determinism, demonstrating that user resistance constitutes professional expertise, revealing system inadequacies. Teacher and student adaptations specify requirements for equitable educational AI, offering essential design knowledge that vendors currently ignore.