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Ontology-Based Adaptive Assessment in Digital Education: Inclusive, Transparent, and Fair AI for Assessment
Abstract
This chapter explores the conceptual, pedagogical, and technical foundations of ontology-based adaptive assessment systems in AI-enhanced digital education. Moving beyond the limitations of conventional AI models, ontology-driven approaches support transparent, personalized, and ethically grounded alternatives by embedding semantic reasoning, explainable feedback, and fairness-aware logic into the assessment process. Drawing on theories such as the Zone of Proximal Development, Cognitive Load Theory, and Universal Design for Learning, this chapter presents a framework for modeling learner behaviors, structuring task progression, and generating context-sensitive feedback. Empirical use cases illustrate improvements in learning efficiency, perceived fairness, and engagement. Benchmarking analysis reveals advantages over traditional models. The chapter also examines institutional readiness, policy development, ethical data governance, and faculty involvement and concludes with future directions for scaling, cultural responsiveness, and interoperability.
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