<![CDATA[Rapid adoption of Artificial Intelligence (AI) in education has transformed teaching, learning, assessment, and academic administration by enabling personalized instruction, intelligent feedback, and data-driven decision-making. Expanding implementation of AI-powered educational systems has simultaneously intensified concerns regarding transparency, fairness, privacy, accountability, and educator responsibility, making trust a fundamental prerequisite for sustainable educational innovation. This study aimed to examine the determinants of trust in AI-powered education systems by investigating the relationships among ethical governance, algorithmic transparency, educator responsibility, institutional accountability, and learner perceptions. A mixed-methods sequential explanatory design was employed, integrating quantitative data collected from 520 participants through validated questionnaires and Structural Equation Modeling with qualitative evidence obtained from interviews, classroom observations, reflective journals, and institutional policy documents. Results demonstrated that algorithmic transparency, educator ethical competence, institutional accountability, and privacy protection significantly strengthened trust in AI-supported education, while educator responsibility partially mediated the relationship between ethical governance and learner trust. Qualitative findings further revealed that transparent communication, professional judgment, and continuous human oversight enhanced acceptance of AI-assisted instructional practices. Findings indicate that trust emerges through the interaction of technological reliability, ethical governance, and pedagogical professionalism rather than technological capability alone. Human-centered implementation supported by transparent institutional policies provides a sustainable foundation for responsible Artificial Intelligence adoption across contemporary educational ecosystems.]]>
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