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Impact Pathways of AI-Supported Instruction on Learning Behaviors, Competence Development, and Academic Achievement in Engineering Education

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  • Yu Wan

    (The College of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China
    Key Laboratory of Ministry of Education for Hydraulic and Water Transport Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

  • Rui Li

    (The College of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China
    Key Laboratory of Ministry of Education for Hydraulic and Water Transport Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

  • Wenjie Li

    (The College of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China
    Key Laboratory of Ministry of Education for Hydraulic and Water Transport Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

  • Hongbo Du

    (The College of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China
    Key Laboratory of Ministry of Education for Hydraulic and Water Transport Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

Abstract

With the increasing integration of artificial intelligence into education, traditional instructional models in Hydraulic Engineering are shifting toward competence- and performance-oriented pedagogy under the New Engineering framework. Rooted in constructivist and learner-centered theories, this study examines how AI-assisted versus traditional instruction influences learning behaviors, competence development, and academic achievement in engineering education through a quasi-experimental study involving 102 undergraduate students. Results indicate that while the AI-assisted group achieved significantly higher Midterm Report Scores and PPT Presentation Scores, no significant difference was observed in Final Exam Scores between the two groups. Multivariate regression and latent profile analysis reveal that AI-assisted instruction enhances Classroom Participation, Data Processing Ability, and Comprehensive Analytical Ability, yet falls short in fostering Practical Problem-solving Ability compared to traditional instruction. Path analysis further indicates that AI-assisted instruction improves Academic Achievement indirectly by promoting Learning Behaviors, which in turn foster Competence Development, ultimately contributing to improved Academic Achievement. By addressing a critical gap in the literature on the mechanisms of AI integration in engineering education, this study underscores the importance of optimizing learning processes rather than merely pursuing outcome enhancement, offering theoretical and practical insights for AI-integrated instructional reform in the context of New Engineering education.

Suggested Citation

  • Yu Wan & Rui Li & Wenjie Li & Hongbo Du, 2025. "Impact Pathways of AI-Supported Instruction on Learning Behaviors, Competence Development, and Academic Achievement in Engineering Education," Sustainability, MDPI, vol. 17(17), pages 1-27, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:17:p:8059-:d:1744180
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    References listed on IDEAS

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