This study explores an experiential learning approach in basic design education by integrating observations of the real world to improve students' learning motivation, self-efficacy, and performance. A quasi-experiment with 28 first-year industrial design students used a single-group pre- and post-test design over a nine-week studio. The short course included four learning units on natural observation for pattern analysis and shape reconstruction. Learning outcomes were assessed through scales, rubrics, T tests and regression analysis, while qualitative feedback was analyzed through text clouds. The results indicated an improvement in self-efficacy but mixed motivation for learning, probably due to the challenges in the reconstruction of shapes using generative rules and CAD tools. Students showed strong interest in field observation and generative rule exploration. The approach has positively influenced self-efficacy and learning performance. Future improvements include the alignment of tasks with the previous knowledge of students, the integration of user-friendly generative AI tools and the introduction of systematic problem-solving methods. © 2025
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