Designing Trustworthy Educational Artificial Intelligence: A Systemic Framework for Explainability, Adaptivity, and Ethical Learning Analytics
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The accelerating integration of artificial intelligence into educational systems has underscored the necessity of designing learning technologies that are not only effective but also trustworthy, transparent, and ethically grounded. Although adaptive and data-driven educational AI systems have demonstrated substantial potential for personalization and performance enhancement, their widespread adoption remains constrained by concerns related to explainability, ethical learning analytics, data privacy, and algorithmic bias. This study proposes a systemic framework for trustworthy educational artificial intelligence that unifies explainability, adaptivity, and ethical governance within a coherent architectural model. Grounded in interdisciplinary theoretical foundations and supported by evidence from systematic literature reviews and empirical case studies, the framework emphasizes human-centered design, transparent decision-making, and continuous ethical oversight. The analysis illustrates how explainable and adaptive AI systems, when coupled with responsible learning analytics, can enhance learner engagement, improve academic outcomes, and foster sustained trust among educators and students. By articulating design principles and architectural layers aligned with established ethical frameworks, this work contributes a robust foundation for developing educational AI systems that balance technological innovation with accountability, fairness, and long-term educational value.
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