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Universal Design for Learning and AI-Enhanced Assessment: Developing a Comprehensive Ontology for Equitable Evaluation
Abstract
This chapter examines the intersection of Universal Design for Learning (UDL) and AI-enhanced assessment to build a more inclusive and equitable evaluation framework in higher education. It critiques traditional assessment practices and explores how UDL principles—combined with AI functionalities such as adaptive assessment, automated feedback, and real-time analytics—can enhance flexibility and support diverse learners. Drawing from literature between 2017 and 2025, the chapter addresses issues such as algorithmic bias, institutional resistance, educator preparedness, and implementation in resource-constrained contexts. It offers a practical, research-informed model that guides policymakers, educators, and technologists in creating more equitable, student-centred assessment systems.
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