AI‐Supported Research‐Led Teaching in Syntegrative Project‐Based Learning: Educator Questioning, Feedback, and Student Learning Outcomes in Authentic Industry Projects

Artificial intelligence (AI) is creating new possibilities for connecting research‐led teaching with authentic industry projects, yet its educational value depends on how educators organize inquiry rather than on technological use alone. This study examined the relationships among AI‐supported research‐led teaching (AIRLT), educator questioning (EQ), educator feedback (EF), student inquiry engagement (SIE), and authentic project learning outcomes (APLO) within Syntegrative Project‐Based Learning. A quantitative cross‐sectional survey was conducted with 898 undergraduate students from 12 universities in Vietnam who had participated in authentic industry or organizational projects involving AI‐supported inquiry and educator guidance. Partial least squares structural equation modeling (PLS‐SEM) showed that AIRLT was positively associated with EQ ( β = 0.642) and EF ( β = 0.611). Both EQ ( β = 0.463) and EF ( β = 0.349) were positively associated with SIE, which, in turn, showed a strong positive association with APLO ( β = 0.689). All six specific indirect effects were significant, indicating statistically significant indirect associations among AI‐supported teaching, educator guidance, student inquiry, and perceived authentic learning outcomes. The findings contribute an educator‐mediated perspective to Syntegrative Education by showing that AI is most educationally meaningful when embedded in purposeful questioning, feedback, and sustained student inquiry. The study highlights the importance of designing AI‐supported industry projects around evidence evaluation, iterative feedback, reflection, and active investigation of authentic problems rather than treating AI use as an educational objective in itself.