<![CDATA[Adaptive learning technologies are increasingly transforming structural engineering education by offering personalized learning experiences and real-time feedback. Grounded in Adaptive Learning Theory and Instructional Design Models, these technologies utilize artificial intelligence (AI) and dynamic assessment tools to tailor instructional content to each student's learning profile, thus enhancing engagement and academic achievement. Key components include immediate feedback, personalized learning paths, and the use of AI techniques such as fuzzy extreme learning machines (ELM) for improved predictive accuracy. This study adopts a quantitative approach with a quasi-experimental pre-test–post-test design, supported by perception questionnaires and log data from adaptive learning platforms. Statistical analysis includes t-tests and linear regression to assess learning gains and engagement levels. The hypothesis posits that adaptive learning significantly improves student outcomes compared to traditional instruction. Preliminary findings indicate heightened engagement, improved conceptual understanding, and strong user satisfaction. These outcomes affirm the value of adaptive learning technologies in structural engineering education while emphasizing the need for further refinement in evaluation practices, accessibility, and ethical design.]]>
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