Beyond transparency: why explainable AI is essential for educational equity in low-resource language communities

Purpose This opinion paper argues that explainable artificial intelligence (XAI) represents a critical bridge for achieving educational equity in higher education for low-resource language communities, but only when implemented through genuine community leadership and cultural sensitivity. Design/methodology/approach Drawing on recent research (2022–2025) and successful indigenous-led AI initiatives, this paper examines the intersection of technical capability, trust dynamics and educational outcomes. It presents an integrated framework combining the CARE Principles for Indigenous Data Governance with the XAI-ED framework for ethical XAI implementation in education. Findings While technical advances have achieved remarkable accuracy (86% for indigenous language recognition), adoption remains limited due to historical mistrust and digital colonialism. Success stories from Te Hiku Media and similar initiatives demonstrate that community-led approaches with transparent AI systems significantly improve educational outcomes while building essential trust. Originality/value This paper contributes a critical perspective on why explainability alone is insufficient; rather, XAI must be embedded within frameworks of indigenous data sovereignty and community empowerment to achieve genuine educational equity.