Assessing the Influence of Fishbone Digital Learning Design (FDLD) and Generative Artificial Intelligence (GenAI) on Enhancing Course Design

This study examines the impact of the fishbone digital Learning Design (FDLD) approach on engineering curriculum design and explores the role of generative artificial intelligence (GenAI) as a peer evaluator. Using Tyler's goal-focused model, GenAI (XIPU AI) simulated the roles of teachers, curriculum committee members, and novice teachers to evaluate FDLD-based design versus traditional design in an undergraduate Artificial intelligence and programming course (Module M). The results show that FDLD significantly improves the clarity of learning objectives through diverse interactive activities (such as experiments, collaborative tasks) and balanced summative assessments consistent with Bloom's classification. GenAI provides a structured, objective assessment that validates the FDLD's adaptability to novice teachers and its consistency with institutional standards. The findings highlight the efficacy of FDLD in promoting higher-order thinking and the potential of GenAI as a scalable evaluator.