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

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 resea...

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Bibliographic Details
Published in:Sustainability 2023-01, Vol.15 (1), p.605
Main Authors: Chai, Ching Sing, Chiu, Thomas K. F, Wang, Xingwei, Jiang, Feng, Lin, Xiao-Fan
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Autonomy
Big Data
Cooperation
Core curriculum
Curricula
Design
Education
Education, Secondary
Educational aspects
Educational research
Educational technology
Hypotheses
Knowledge
Machine learning
Motivation
Natural language
Reliability analysis
Schools
Secondary school students
Secondary schools
Social discrimination learning
Social norms
Students
Surveys
Sustainability
Theory of planned behavior
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su15010605