Integrating AIGC into product design ideation teaching: An empirical study on self-efficacy and learning outcomes

The emergence of artificial intelligence-generated content (AIGC) in the realm of education, notably in product design, signifies a watershed moment, heralding significant enhancements over conventional pedagogies by potentially catalyzing unparalleled innovation. This investigation assesses the ramifications of assimilating AIGC into product design instruction, focusing on its advantages, constraints, and consequent influence on students' design cognition across a spectrum of proficiency levels. The study encompassed 119 scholars with a focus on product or industrial design, delineated into three distinct echelons of proficiency. Utilizing Technology-mediated Learning Theory, an empirical field study was initiated to explore AIGC's impact on self-efficacy, ideation volume, innovation, diversity, and the aggregate quality of outcomes, taking into account the divergence in pedagogical strategies and student competency tiers. AIGC notably augmented students' self-efficacy, ideation, novelty, and variety, albeit with a potential diminution in ideational quality. Disparities in self-efficacy, volume of ideas, and their caliber were discernibly evident across varying tiers of competency. AIGC markedly fosters innovation within product design pedagogy, demonstrating its ascendancy over traditional instructional methods in catalyzing scholastic innovation. However, orthodox teaching methodologies retain their critical role in the cultivation of problem-solving acumen. Personalized support, particularly for those demonstrating lower self-efficacy, is paramount in amplifying their creative ideation through bespoke pedagogical strategies, thus maximizing the utility of AIGC integration.