Abstract
K-12 artificial intelligence (AI) education requires cultivating students’ computational thinking in the school curriculum so as to transfer their computational thinking to diverse problems and authentic contexts. However, students may be limited by traditional computational thinking development activities because they may have a lower degree of computational thinking efficacy for persistent learning of AI when encountering difficulties (computational thinking efficacy in learning AI). Accordingly, this study aimed to explore the relationships among Chinese secondary school students’ computational thinking efficacy in learning AI, their AI literacy, and approaches to learning AI. Structural equation modeling was adopted to examine the mediation effect. Data were gathered from 509 Chinese secondary school students, and the confirmatory factor analyses showed that the measures had high reliability and validity. The results revealed that AI literacy was positively related to students’ computational thinking efficacy in learning AI, which was mediated by more sophisticated approaches to learning AI, contributing to the current understanding of learning AI. It is crucial to focus on students’ AI literacy and deep approaches (e.g., engaging in authentic AI contexts with systematic learning activities for in-depth understanding of AI knowledge) rather than surface approaches (e.g., memorizing AI knowledge) to develop their high-level computational thinking efficacy in learning AI. Implications for designing the AI curriculum are discussed.
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Data Availability
The datasets used and/or analyzed during the current study are not publicly available due to their personal and private nature but are available from the corresponding author on reasonable request.
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Acknowledgements
This research was funded by the National Natural Science Foundation of China [grant number 62007010]; the Key program of National Natural Science Foundation of China [grant number 62237001]; the National Key R&D Program of China [grant number 2022YFC3303605]; the Science and Technology Projects in Guangzhou [grant number 202102021217]; Guangzhou Youths’ Participation in Rural Revitalization Research: The I-SEED “Internet Plus” Cloud Public Welfare to Empower Rural Education Revitalization; Teaching Quality Project of South China Normal University: A Study on Teacher Professional Development for Teaching Artificial Intelligence Courses Based on TPACK [grant number 177]; the Special Funds of Climbing Program regarding the Cultivation of Guangdong College Students’ Scientific and Technological Innovation [grant number pdjh2023a0139]. The authors express their gratitude to the anonymous reviewers and editors for their helpful comments about this paper. The authors would like to express our gratitude to Professor Chai Ching Sing from the Chinese University of Hong Kong for his assistance. His professional knowledge enabled us to improve our research. We also want to express their gratitude to Wenyi Li for her helpful review of this paper.
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Xiao-Fan Lin: Conceptualization, Investigation, Software, Data Curation, Writing - Review & Editing. Yue Zhou: Writing - Original Draft, Editing, Data analysis. Weipeng Shen: Writing, Editing, Data analysis. Guoyu Luo: Writing, Methodology. Xiaoqing Xian: Writing, Data analysis. Bo Pang: Writing - Review & Editing, Instruction.
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Lin, XF., Zhou, Y., Shen, W. et al. Modeling the structural relationships among Chinese secondary school students’ computational thinking efficacy in learning AI, AI literacy, and approaches to learning AI. Educ Inf Technol 29, 6189–6215 (2024). https://doi.org/10.1007/s10639-023-12029-4
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DOI: https://doi.org/10.1007/s10639-023-12029-4