A Case Study on SAL-Based Teaching and Learning in a Functional Clothing and Materials Course Using Generative AI

With the rapid development of big data and generative artificial intelligence (AI), education in the apparel industry is evolving beyond traditional theoretical instruction. This study explores the application of Smart Action Learning (SAL) and generative AI tools in the "Functional Clothing and Materials" course to enhance student engagement, problem-solving abilities, and practical skill acquisition. SAL, a student-centered learning approach, incorporates blended learning, flipped learning, and problem-based learning (PBL) methodologies to foster self-directed and cooperative learning. The study was conducted over an eight-week period with second-year students in an apparel major, integrating AI-based problem-solving techniques into coursework. The course design included a five-stage instructional framework: instructor-led lectures, problem identification, AI-assisted research, collaborative solution development, and final presentations. The effectiveness of this approach was evaluated through surveys measuring self-directed learning ability, cooperative learning ability, problem-solving ability, and academic achievement. Results indicate that students demonstrated high levels of engagement, improved problem-solving skills, and increased learning satisfaction through AI-assisted collaborative projects. The findings suggest that incorporating generative AI into fashion education enhances creative thinking, industry readiness, and adaptability to emerging trends.