Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-Efficacy - Shao-Hsun Chang; Kai-Chao Yao; Yao-Ting Chen; Cheng-Yang Chung; Wei-Lun Huang; Wei-Sho Ho (Information)
The integration of artificial intelligence (AI) technologies into education has gained increasing attention, yet limited research examines how the curriculum design can enhance learning outcomes and influence learners’ intentions to continue AI learning. This study addresses this gap by integrating the theory of planned behavior, technology acceptance model, theories of motivation, and computer self-efficacy to explore the factors affecting learners’ behavioral intentions in AI education. Using the AI course quality as the primary antecedent and “intention to continue taking courses” as the dependent variable, the study investigates the structural relationships and mediating variables between these factors. Data were collected through a stratified random sampling method from 19 universities in Taiwan, involving 200 students who had completed five core AI-related courses, including artificial intelligence, machine learning, internet of things, big data, and robotics. The analysis, conducted using PLS-SEM, revealed that AI course quality directly and indirectly influences learners’ behavioral intentions through mediating variables such as learning satisfaction, computer self-efficacy, technological literacy, and computer learning motivation. Moreover, AI course quality exerted a significant positive effect on computer motivation, which, in turn, influenced self-efficacy and learning outcomes. These findings provide valuable insights into the antecedents and processes shaping learners’ intentions to continue AI learning, offering practical and theoretical implications for AI education.