Universal Alignment Fails in Global Classrooms: Cross-Cultural Blind Spots in EdTech AI

The global expansion of educational AI often falls into a "portability trap,'' where dominant EdTech systems aligned to WEIRD (Western, Educated, Industrialized, Rich, and Democratic) populations impose culturally specific assumptions on diverse global classrooms. We argue that these cross-cultural failures are fundamentally alignment problems: standard pipelines like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) collapse legitimate educational disagreements over pedagogy, privacy, and language into a single, culturally rigid reward target. To bridge theory and practice, we map documented classroom harms—linguistic marginalization, biometric proctoring bias, pedagogical mismatch, and socio-emotional neglect—directly to specific ML failure modes, including distributional collapse and reward misspecification. In response, we propose a four-pillar socio-technical roadmap for pluralistic EdTech: modular objective design, sovereign data infrastructures, localized impact verification, and human mediation. Ultimately, responsible educational AI requires a universal rights floor paired with pluralistic systems that can be explicitly steered toward legitimate local values, rather than assimilating them into an algorithmic monoculture.