Promoting Students’ Well-Being by Developing Their Readiness for the Artificial Intelligence Age

Yun Dai, Ching-Sing Chai, Pei-Yi Lin, Morris Siu-Yung Jong, Yanmei Guo and Jianjun Qin
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Yun Dai: Department of Curriculum and Instruction, The Chinese University of Hong Kong, Hong Kong, China
Ching-Sing Chai: Department of Curriculum and Instruction, The Chinese University of Hong Kong, Hong Kong, China
Pei-Yi Lin: Department of Curriculum and Instruction, The Chinese University of Hong Kong, Hong Kong, China
Morris Siu-Yung Jong: Department of Curriculum and Instruction, The Chinese University of Hong Kong, Hong Kong, China
Yanmei Guo: Teacher Training and Development Centre of Chaoyang District, Beijing 100082, China
Jianjun Qin: School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100081, China

Sustainability, 2020, vol. 12, issue 16, 1-15

Abstract: This study developed and validated an instrument to measure students’ readiness to learn about artificial intelligence (AI). The designed survey questionnaire was administrated in a school district in Beijing after an AI course was developed and implemented. The collected data and analytical results provided insights regarding the self-reported perceptions of primary students’ AI readiness and enabled the identification of factors that may influence this parameter. The results indicated that AI literacy was not predictive of AI readiness. The influences of AI literacy were mediated by the students’ confidence and perception of AI relevance. The students’ AI readiness was not influenced by a reduction in their anxiety regarding AI and an enhancement in their AI literacy. Male students reported a higher confidence, relevance, and readiness for AI than female students did. The sentiments reflected by the open-ended responses of the students indicated that the students were generally excited to learn about AI and viewed AI as a powerful and useful technology. The student sentiments confirmed the quantitative findings. The validated survey can help teachers better understand and monitor students’ learning, as well as reflect on the design of the AI curriculum and the associated teaching effectiveness.

Keywords: student readiness for AI; AI anxiety; AI literacy; AI education; student survey (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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