2026 ASEE Annual Conference & Exposition

Artificial intelligence (AI) tools and chatbots have simultaneously introduced opportunities and challenges in the context of construction engineering and management (CEM) education. This study examined the integration of ScheduleAI, a specialized chatbot to support senior construction management (CM) students in a Project Scheduling course. The study designed and compared two pedagogical approaches: (1) one section (25 students, Group 1) received guided integration, in which Instructor A actively scaffolded chatbot use within in-class learning activities; (2) other parallel sections (76 students, Group 2) were taught by Instructor B, who provided access to the same AI tool without offering direct guidance on its use. All course sections were lab-based and delivered in a computer lab where students met once per week in a three-hour session.

This study aims to investigate how different instructor guidance levels relate to students’ engagement with AI tools and their perceptions of learning in project scheduling. The research lies in instructional technologies in construction education, particularly the interaction between pedagogical scaffolding and student engagement in AI-integrated teaching and learning environments. In this project scheduling course, CM students developed competencies in areas such as work breakdown structure (WBS) development, activity duration estimation, and resource planning.

Pre- and post-course surveys were conducted to assess the role of AI chatbots in students’ learning experiences. Results indicate that while both instructional approaches exhibited generally positive attitudes toward AI chatbots, guided integration was associated with higher perceived usefulness, more frequent and consistent chatbot use, and stronger perceptions of learning improvement. Unguided access to ScheduleAI was associated with relatively less substantial shifts in students’ perceived learning benefits and greater variability in usage patterns. This work contributes empirical evidence that can inform more pedagogically grounded approaches to integrating AI into CEM education.