AI-assisted TRIZ teaching with scaffolded fade-out: A quasi-experimental study in undergraduate mechanical engineering

TRIZ (Theory of Inventive Problem Solving) has earned recognition as a structured innovation methodology, yet its integration into undergraduate engineering curricula remains limited-students frequently struggle with parameter extraction and contradiction formulation, the very gates to effective TRIZ application. This study proposes a scaffolded fade-out framework that deploys an AI dialogue assistant in three progressively withdrawn phases-Proactive Guidance, Reactive Response, and On-Demand Consultation-over a four-week intervention. A quasi-experimental design (experimental group n = 42, control group n = 40) was implemented in a Mechanical Innovation Design course. Results indicate that the experimental group significantly outperformed the control group on total TRIZ modeling competence (ANCOVA F(1,79) = 18.43, p < 0.001, Cohen's d = 0.89), with large effects on parameter extraction (d = 1.15) and contradiction identification (d = 1.19). Solution innovation scores reversed (d = −0.36), which may partially reflect an anchoring effect from AI-provided inventive principles, though this interpretation requires further validation. SOLO taxonomy analysis corroborated deeper structural understanding in the experimental group. These findings suggest that scaffolded AI assistance can accelerate TRIZ skill acquisition within the conditions studied, though the fade-out protocol requires careful calibration to avoid constraining creative divergence.t