Enhancing student creativity in AIGC design education: the mediating role of learning motivation in reverse engineering pedagogy

Introduction: The widespread application of artificial intelligence generated content (AIGC) technology in design education presents challenges in balancing technological dependency and autonomous innovation during creativity development. Establishing a dynamic equilibrium between technological empowerment and innovation capacity is critical in educational practice. This study examines how reverse engineering pedagogy (REP) stimulates learning motivation to foster creativity and proposes a multidimensional creativity evaluation framework. Methods: A quasi-experimental design with intact class-based quasi-random allocation was employed, involving 80 first-year animation majors from two intact classes (40 students per class) assigned to an experimental group (REP intervention) and a control group (traditional instruction). Quantitative assessments using multidimensional creativity and learning motivation scales, supported by statistical analysis, revealed inter-variable relationships. Results: The results indicated that the REP framework, comprising structural analysis, functional mapping, and creative reconstruction, significantly enhanced creative design thinking. Learning motivation partially mediated the effect of REP on creativity, confirming synergistic influences of intrinsic and extrinsic motivation. Discussion: The study proposes a multidimensional creativity evaluation framework integrating REP and learning motivation, and provides theoretical-practical guidance for pedagogical innovation in AIGC-driven design education.