Mathematics education

Biology

Social psychology

China

Pedagogy

Goal theory

Beijing

Motivation to learn

Computer science

Curriculum

Structural equation modeling

Paleontology

Physics

Test (biology)

Intrinsic motivation

Psychology

Relevance (law)

Astronomy

Applied psychology

Political science

Primary (astronomy)

Artificial intelligence

Machine learning

Computers and Education Artificial Intelligence

The recent advances in artificial intelligence (AI) present both challenges and opportunities for educational practitioners. A new AI curriculum has been developed and piloted in many primary schools in Beijing, China. The present study had two aims: (1) to test the factor structure of students’ motivation to learn AI and (2) to examine possible gender differences in students’ motivation to learn AI. This online questionnaire–based research recruited 420 primary students from the piloting schools. Structural equation modeling was employed to test a hypothesized model comprising six motivational factors and strategies: (1) intrinsic motivation, (2) career motivation, (3) attention, (4) relevance, (5) confidence, and (6) satisfaction. The study discovered intrinsic motivation to have the strongest influence on career motivation, while the motivational strategies of attention, relevance, and confidence also influenced career motivation. Additionally, compared with female students, male students scored higher in terms of motivational factors and strategies. The findings serve as a reference for the future development of AI curricula and instruction.

Modeling the structural relationship among primary students’ motivation to learn artificial intelligence