The Effectiveness of AI-Based Visual Recipes on the Practical Skills of Culinary Students

The integration of artificial intelligence (AI) in vocational education offers innovative opportunities to enhance practical skill acquisition, particularly in culinary training. This study examines the effectiveness of AI-based visual recipes in improving the practical skills of culinary students. The research adopts a quantitative explanatory approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) with 150 culinary students who had experienced AI-based visual guidance in practical cooking sessions. The study investigates the relationships among AI-based visual cooking guidance, cognitive load, student engagement, and effective learning as a reflection of practical skill development. The results show that AI-based visual guidance has asignificant positive effect on cognitive load and student engagement. Student engagement significantly enhances effective learning, indicating its critical role in improving culinary students’ practical skills. In contrast, cognitive load does not significantly affect learning outcomes. Mediation analysis reveals that student engagement significantly mediates the relationship between AI-based visual guidance and effective learning, while cognitive load does not. These findings suggest that AI-based visual recipes are effective in enhancing practical culinary skills primarily through increased engagement rather than cognitive load reduction. The study contributes to the development of AI-supported vocational learning and provides implications for improving technology-enhanced practical education.