A generalizable OBE-AIGC-PBL pedagogical architecture for theory-intensive engineering courses: Quasi-experimental validation of a strictly aligned four-stage four-layer scaffolding system

Theory-intensive engineering courses face persistent challenges including obscure abstract concepts, disconnection between theory and practice, and insufficient experimental conditions. To address these deficiencies, this study developed and validated a pedagogical architecture integrating Outcome-Based Education (OBE), Project-Based Learning (PBL) and Artificial Intelligence Generated Content (AIGC), termed the OBE-AIGC-PBL framework. The architecture integrates a four-stage inquiry cycle and a four-layer AIGC cognitive scaffolding system to support systematic and deep learning. A quasi-experiment was conducted involving 66 undergraduate students from an Automotive Theory course, with 34 in the experimental group and 32 in the control group (traditional PBL without AIGC). Results indicate that the proposed pedagogical architecture significantly improved students’ comprehensive engineering competencies (Cohen's d = 0.96), particularly in simulation modeling capability (d = 1.13) and theoretical knowledge mastery (d = 1.02). The experimental group achieved a 22.1% higher score in comprehensive engineering design problems and completed 7 times more design iterations than the control group. Notably, Levene's test confirmed that the architecture significantly reduced score dispersion (SD = 7.8 vs 10.2, F(1,64) = 4.21, p = 0.044) and demonstrated favorable educational equity effects by narrowing achievement gaps among students. This study extends cognitive load theory and zone of proximal development theory into intelligent learning environments, repositioning AIGC as a core instructional element. The validated architecture provides a replicable, scalable, and theoretically grounded solution for improving teaching quality in theory-intensive engineering education.