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

心理学 操作化 验证性因素分析 结构方程建模 构造(python库) 计划行为理论 控制(管理) 社会心理学 数学教育 结构效度 应用心理学 发展心理学 心理测量学 人工智能 计算机科学 哲学 认识论 机器学习 程序设计语言
作者
Ching Sing Chai,Xingwei Wang,Chang Xu
出处
期刊:Mathematics [Multidisciplinary Digital Publishing Institute]
卷期号:8 (11): 2089-2089 被引量:197
标识
DOI:10.3390/math8112089
摘要

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.
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