The Effects of a Generative AI‐Enabled CDIO Teaching Model on Undergraduates' Computational Thinking and Individual Psychological Constructs

ABSTRACT With the rapid advancement of artificial intelligence, generative AI (AIGC) has emerged as a transformative tool in education, particularly in engineering disciplines where it demonstrates significant pedagogical potential. The CDIO (Conceive–Design–Implement–Operate) teaching model, rooted in experiential and project‐based learning, emphasizes the development of students' integrated engineering competencies. However, engineering education remains challenging for many Chinese university students, despite the availability of online and collaborative learning resources, thereby underscoring the need for enhanced instructional strategies supported by advanced technologies. However, empirical research on the application of generative artificial intelligence in engineering education, particularly regarding its effects on individual psychological constructs, remains limited. This study, conducted over one semester in a data mining course, examines the impact of the AIGC‐supported CDIO teaching model on students' computational thinking, learning motivation, engagement, and cognitive load. The participants included 76 s‐year undergraduates from a teacher training university in China. The experimental group ( n = 27) adopted the AIGC‐CDIO teaching model, while Control Group 1 ( n = 24) followed the traditional CDIO model, and Control Group 2 ( n = 25) engaged solely in collaborative learning. Results from ANOVA analysis revealed that the experimental group demonstrated significant improvements in intrinsic motivation, behavioral and emotional engagement, and computational thinking abilities (including algorithmic thinking, critical thinking, and problem‐solving skills), outperforming both control groups. Moreover, the experimental group exhibited significantly lower cognitive load. These major findings highlight the pedagogical effectiveness of the AIGC‐CDIO approach in enhancing student engagement and reducing mental effort. The findings provide robust empirical support for the integration of AIGC into CDIO‐based engineering education. This study contributes to the emerging literature on AI‐assisted pedagogy by offering evidence‐based insights into the interplay between technological mediation, psychological factors, and cognitive skill development. Implications for future research include deeper investigations into the mechanisms linking AIGC use to learning outcomes, longitudinal tracking of computational thinking development, and the refinement of adaptive instructional models across diverse learner profiles.