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Modeling Chinese Secondary School Students’ Behavioral Intentions to Learn Artificial Intelligence with the Theory of Planned Behavior and Self-Determination Theory

Author

Listed:
  • Ching Sing Chai

    (Department of Curriculum and Instruction, Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong, China)

  • Thomas K. F. Chiu

    (Department of Curriculum and Instruction, Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong, China)

  • Xingwei Wang

    (College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Feng Jiang

    (College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Xiao-Fan Lin

    (School of Education Information Technology, South China Normal University, Guangzhou 510631, China
    Guangdong Engineering Technology Research Center of Smart Learning, Guangzhou 510631, China
    Guangdong Provincial Institute of Elementary Education and Information Technology, Guangzhou 510631, China)

Abstract

It has become essential for current learners to gain basic literacy and competencies for artificial intelligence (AI). While educators and education authorities are beginning to design AI curricula, empirical studies on students’ perceptions of learning AI are still rare. This study examined a research model that synthesized the theory of planned behavior and the self-determination theory. The model explains students’ behavioral intention to learn AI. The model depicts the interrelationships among the factors of AI knowledge, programming efficacy, autonomy, AI for social good, and learning resources. The participants were 509 secondary school students who completed a series of AI lessons and a survey. The factor analyses revealed that our proposed instrument in the survey possesses construct validity and good reliability. Our further analysis supported that design of learning resources, autonomy, and AI for social good predicted behavioral intention to learn AI. However, unexpected findings were presented (i.e., AI knowledge failed to predict social good and programming efficacy negatively influenced autonomy). The findings serve as a reference for the future development of AI education in schools by noting that the design of the AI curriculum should take students’ needs and satisfaction into account to facilitate their continuous development of AI competencies.

Suggested Citation

  • Ching Sing Chai & Thomas K. F. Chiu & Xingwei Wang & Feng Jiang & Xiao-Fan Lin, 2022. "Modeling Chinese Secondary School Students’ Behavioral Intentions to Learn Artificial Intelligence with the Theory of Planned Behavior and Self-Determination Theory," Sustainability, MDPI, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:605-:d:1019206
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    References listed on IDEAS

    as
    1. Ching Sing Chai & Xingwei Wang & Chang Xu, 2020. "An Extended Theory of Planned Behavior for the Modelling of Chinese Secondary School Students’ Intention to Learn Artificial Intelligence," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    2. Zhaoyi Pei & Songhao Piao & Mohammed El Habib Souidi & Muhammad Zuhair Qadir & Guo Li, 2018. "SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation," Sustainability, MDPI, vol. 10(8), pages 1-18, August.
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    Cited by:

    1. Shuai Zhang & Jiannan Du & Huiji Yue & Gui’an Li & Dian Zhang, 2023. "Study on the National Identity Education Intentions of Pre-Service Teachers: Evidence from PLS-SEM and fsQCA," Sustainability, MDPI, vol. 15(16), pages 1-23, August.

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