Exploring the Role of Artificial Intelligence in Enhancing Asynchronous Learning for Construction Management Students
Year: 2026
Type: article
Abstract: 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.
Authors: Lawal, Nurudeen Segun, Burgett, Joseph M.
Institution: Clemson University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7166555252
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