Modeling Chinese Secondary School Students’ Behavioral Intentions to Learn Artificial Intelligence with the Theory of Planned Behavior and Self-Determination Theory

Ching Sing Chai, Thomas K. F. Chiu, Xingwei Wang, Feng Jiang and Xiao-Fan Lin ()
Additional contact information
Ching Sing Chai: Department of Curriculum and Instruction, Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong, China
Thomas K. F. Chiu: Department of Curriculum and Instruction, Centre for Learning Sciences and Technologies, 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
Feng Jiang: College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China
Xiao-Fan Lin: School of Education Information Technology, South China Normal University, Guangzhou 510631, China

Sustainability, 2022, vol. 15, issue 1, 1-16

Abstract: It has become essential for current learners to gain basic literacy and competencies for artificial intelligence (AI). While educators and education authorities are beginning to design AI curricula, empirical studies on students’ perceptions of learning AI are still rare. This study examined a research model that synthesized the theory of planned behavior and the self-determination theory. The model explains students’ behavioral intention to learn AI. The model depicts the interrelationships among the factors of AI knowledge, programming efficacy, autonomy, AI for social good, and learning resources. The participants were 509 secondary school students who completed a series of AI lessons and a survey. The factor analyses revealed that our proposed instrument in the survey possesses construct validity and good reliability. Our further analysis supported that design of learning resources, autonomy, and AI for social good predicted behavioral intention to learn AI. However, unexpected findings were presented (i.e., AI knowledge failed to predict social good and programming efficacy negatively influenced autonomy). The findings serve as a reference for the future development of AI education in schools by noting that the design of the AI curriculum should take students’ needs and satisfaction into account to facilitate their continuous development of AI competencies.

Keywords: artificial intelligence; behavioral intention; competence; autonomy; social good (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/1/605/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/1/605/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2022:i:1:p:605-:d:1019206

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().