Transforming IP Design Education with AI: A Comparative Study of AI-Integrated and Traditional Flipped Learning Models

This study examines the impact of AI-enhanced flipped learning on student engagement, learning effectiveness, and key competencies development in the IP Design course at Wuhan Textile University. With the increasing demand for personalized learning, this research evaluates how AI-supported flipped learning addresses challenges in traditional teaching, such as low participation and inadequate feedback. A mixed-methods approach, including surveys, interviews, and performance metrics, compares two student cohorts: the 2020 cohort (traditional flipped learning) and the 2021 cohort (AI-integrated flipped learning). Results show that AI tools significantly improved creativity, critical thinking, collaboration, and cultural understanding. However, issues like AI over-reliance and gender-based engagement differences emerged, indicating the need for careful AI implementation. This study contributes to the growing literature on AI in education, offering insights into the practical application of AI-driven flipped classrooms. Future research could focus on refining AI tools, addressing gender disparities, and exploring long-term student development impacts.