Exploring the Role of Artificial Intelligence in Enhancing Asynchronous Learning for Construction Management Students

Large language models are increasingly used to support instruction, yet evidence in construction management education remains limited. This study evaluates an AI-supported instructional model implemented in an asynchronous construction management course and examines student perceptions and academic performance. The model combined four coordinated resources: (1) AI voice-over lectures, (2) an AI-derived course text, (3) lecture transcripts and study notes, and (4) a custom course chatbot. Resources were designed using Cognitive Load Theory to improve clarity, reduce extraneous load, and provide timely help without continuous live instruction. Students completed a post-course survey, and a subset participated in semi-structured interviews. Survey results showed generally positive perceptions of clarity, engagement, and usefulness for exam preparation; interview themes emphasized convenience, adaptive support, and the need for verification and instructor oversight. To explore learning outcomes, exam scores from the AI-supported online section were compared with those from a traditional in-person section using the same assessments; performance was comparable across the four exams. Overall, results suggest that a purposefully designed AI “content ecosystem” can enhance the asynchronous learning experience without sacrificing measurable course performance.