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An Extended Theory of Planned Behavior for the Modelling of Chinese Secondary School Students’ Intention to Learn Artificial Intelligence

Author

Listed:
  • Ching Sing Chai

    (Department of Curriculum and Instruction, 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)

  • Chang Xu

    (Faculty of Education, Beijing Normal University, Beijing 100875, China)

Abstract

Artificial Intelligence (AI) is currently changing how people live and work. Its importance has prompted educators to begin teaching AI in secondary schools. This study examined how Chinese secondary school students’ intention to learn AI were associated with eight other relevant psychological factors. Five hundred and forty-five secondary school students who have completed at least one cycle of AI course were recruited to participate in this study. Based on the theory of planned behavior, the students’ AI literacy, subjective norms, and anxiety were identified as background factors. These background factors were hypothesized to influence the students’ attitudes towards AI, their perceived behavioral control, and their intention to learn AI. To provide more nuanced understanding, the students’ attitude towards AI was further delineated as constituted by their perception of the usefulness of AI, the potential of AI technology to promote social good, and their attitude towards using AI technology. Similarly, the perceived behavioral control was operationalized as students’ confidence towards learning AI knowledge and optimistic outlook of an AI infused world. Relationships between the factors were theoretically illustrated as a model that depicts how students’ intention to learn AI was constituted. Two research questions were then formulated. Confirmatory factor analysis was employed to validate that multi-factor survey, followed by structural equational modelling to ascertain the significant associations between the factors. The confirmatory factor analysis supports the construct validity of the questionnaire. Twenty-five out of the thirty-three hypotheses were supported through structural equation modelling. The model helps researchers and educators to understand the factors that shape students’ intention to learn AI. These factors should be considered for the design of AI curriculum.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2089-:d:449326
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    References listed on IDEAS

    as
    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. Yun Dai & Ching-Sing Chai & Pei-Yi Lin & Morris Siu-Yung Jong & Yanmei Guo & Jianjun Qin, 2020. "Promoting Students’ Well-Being by Developing Their Readiness for the Artificial Intelligence Age," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
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    Cited by:

    1. Xin Li & Michael Yi-chao Jiang & Morris Siu-yung Jong & Xinping Zhang & Ching-sing Chai, 2022. "Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study," IJERPH, MDPI, vol. 19(14), pages 1-17, July.
    2. Xiao-Fan Lin & Lu Chen & Kan Kan Chan & Shiqing Peng & Xifan Chen & Siqi Xie & Jiachun Liu & Qintai Hu, 2022. "Teachers’ Perceptions of Teaching Sustainable Artificial Intelligence: A Design Frame Perspective," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    3. 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.

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