Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-Efficacy
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...
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2025-01-01
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| author | Shao-Hsun Chang Kai-Chao Yao Yao-Ting Chen Cheng-Yang Chung Wei-Lun Huang Wei-Sho Ho |
| author_facet | Shao-Hsun Chang Kai-Chao Yao Yao-Ting Chen Cheng-Yang Chung Wei-Lun Huang Wei-Sho Ho |
| author_sort | Shao-Hsun Chang |
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| description | 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. |
| format | Article |
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| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-0eb852c0d528459fab7a268858e07dec2025-01-24T13:35:16ZengMDPI AGInformation2078-24892025-01-011615010.3390/info16010050Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-EfficacyShao-Hsun Chang0Kai-Chao Yao1Yao-Ting Chen2Cheng-Yang Chung3Wei-Lun Huang4Wei-Sho Ho5Department of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City 500208, TaiwanThe 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.https://www.mdpi.com/2078-2489/16/1/50artificial intelligence (AI)technological literacyplanned behavior theorymotivation theorytechnology acceptance model |
| spellingShingle | Shao-Hsun Chang Kai-Chao Yao Yao-Ting Chen Cheng-Yang Chung Wei-Lun Huang Wei-Sho Ho Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-Efficacy Information artificial intelligence (AI) technological literacy planned behavior theory motivation theory technology acceptance model |
| title | Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-Efficacy |
| title_full | Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-Efficacy |
| title_fullStr | Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-Efficacy |
| title_full_unstemmed | Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-Efficacy |
| title_short | Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-Efficacy |
| title_sort | integrating motivation theory into the aied curriculum for technical education examining the impact on learning outcomes and the moderating role of computer self efficacy |
| topic | artificial intelligence (AI) technological literacy planned behavior theory motivation theory technology acceptance model |
| url | https://www.mdpi.com/2078-2489/16/1/50 |
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