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Secondary school students’ intentions to learn AI: testing moderation effects of readiness, social good and optimism

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Abstract

As Artificial Intelligence (AI) is rapidly integrated into existing technologies which has brought forth the fourth industrial and learning revolution, designing curriculum for AI education has become an important strategic development for education authorities throughout the world. Framed broadly from the Theory of Planned Behavior, this study examined a structural equational model to establish the interrelationships of students’ perceived usefulness, attitude towards using AI, subjective norms to learn about AI, basic literacy about AI and their behavioral intention to learn about AI. In addition, it examines the moderation effects of readiness, social good, and optimism on the research model. The findings confirm the hypothesized model. In addition, various moderation effects were found among students’ perception of readiness, social good, and optimism for AI. The implications of the study point towards the need to consider these factors in designing AI curriculum to foster students’ behavioral intention to learn AI.

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Acknowledgements

The authors would like to express gratitude to participants in this study.

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Authors and Affiliations

  1. School of Foreign Languages, Qingdao University, Qingdao, China

    Fang Huang

  2. Faculty of Education, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR

    Chai Ching Sing, Timothy Teo & Thomas K. F. Chiu

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

    Wang Xing wei

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Sing, C.C., Teo, T., Huang, F. et al. Secondary school students’ intentions to learn AI: testing moderation effects of readiness, social good and optimism. Education Tech Research Dev 70, 765–782 (2022). https://doi.org/10.1007/s11423-022-10111-1

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