Analyzing the network structure of students’ motivation to learn AI: a self-determination theory perspective
Abstract Motivation is a key driver of learning. Prior work on motivation has mostly focused on conventional learning contexts that did not necessarily involve AI. Hence, little is known about students’ motivation to learn AI. This study examined the structure of students’ AI motivational system usi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Science of Learning |
| Online Access: | https://doi.org/10.1038/s41539-025-00339-w |
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| author | Jiajing Li Jianhua Zhang Ching Sing Chai Vivian W. Y. Lee Xuesong Zhai Xingwei Wang Ronnel B. King |
| author_facet | Jiajing Li Jianhua Zhang Ching Sing Chai Vivian W. Y. Lee Xuesong Zhai Xingwei Wang Ronnel B. King |
| author_sort | Jiajing Li |
| collection | DOAJ |
| description | Abstract Motivation is a key driver of learning. Prior work on motivation has mostly focused on conventional learning contexts that did not necessarily involve AI. Hence, little is known about students’ motivation to learn AI. This study examined the structure of students’ AI motivational system using self-determination theory as the theoretical framework. Self-determination theory posits that there are qualitatively distinct types of motivation, including intrinsic motivation, identified regulation, introjected regulation, external regulation, and amotivation. Students' motivation, in turn, is strongly shaped by whether their basic psychological needs for competence, autonomy, and relatedness are satisfied. We used network analysis to explore the structure of students’ AI motivation. Participants included 1465 students from 47 universities. Introjected regulation was central to the AI motivational system but intrinsic motivation was less central. This meant that many students learned AI primarily out of guilt or shame and not because of personal enjoyment. Furthermore, competence satisfaction seemed more important than autonomy and relatedness satisfaction in AI-enriched learning environments. Hence, key practical implications include the need to have clear goals and standards as well as to build students' competence in using AI tools. This study enriches the AI education literature by focusing on students' motivational systems and suggesting ways to cultivate better engagement with AI. |
| format | Article |
| id | doaj-art-fe49fdf0a658415db39ae2449c2ebefa |
| institution | DOAJ |
| issn | 2056-7936 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Science of Learning |
| spelling | doaj-art-fe49fdf0a658415db39ae2449c2ebefa2025-08-20T03:04:15ZengNature Portfolionpj Science of Learning2056-79362025-07-0110111010.1038/s41539-025-00339-wAnalyzing the network structure of students’ motivation to learn AI: a self-determination theory perspectiveJiajing Li0Jianhua Zhang1Ching Sing Chai2Vivian W. Y. Lee3Xuesong Zhai4Xingwei Wang5Ronnel B. King6College of Education for the Future, Beijing Normal UniversityDepartment of Curriculum and Instruction, Faculty of Education, The Chinese University of Hong KongDepartment of Curriculum and Instruction, Faculty of Education, The Chinese University of Hong KongCentre for Learning Enhancement And Research, The Chinese University of Hong KongCollege of Education, Zhejiang UniversitySchool of Environment, Tsinghua UniversityDepartment of Curriculum and Instruction, Faculty of Education, The Chinese University of Hong KongAbstract Motivation is a key driver of learning. Prior work on motivation has mostly focused on conventional learning contexts that did not necessarily involve AI. Hence, little is known about students’ motivation to learn AI. This study examined the structure of students’ AI motivational system using self-determination theory as the theoretical framework. Self-determination theory posits that there are qualitatively distinct types of motivation, including intrinsic motivation, identified regulation, introjected regulation, external regulation, and amotivation. Students' motivation, in turn, is strongly shaped by whether their basic psychological needs for competence, autonomy, and relatedness are satisfied. We used network analysis to explore the structure of students’ AI motivation. Participants included 1465 students from 47 universities. Introjected regulation was central to the AI motivational system but intrinsic motivation was less central. This meant that many students learned AI primarily out of guilt or shame and not because of personal enjoyment. Furthermore, competence satisfaction seemed more important than autonomy and relatedness satisfaction in AI-enriched learning environments. Hence, key practical implications include the need to have clear goals and standards as well as to build students' competence in using AI tools. This study enriches the AI education literature by focusing on students' motivational systems and suggesting ways to cultivate better engagement with AI.https://doi.org/10.1038/s41539-025-00339-w |
| spellingShingle | Jiajing Li Jianhua Zhang Ching Sing Chai Vivian W. Y. Lee Xuesong Zhai Xingwei Wang Ronnel B. King Analyzing the network structure of students’ motivation to learn AI: a self-determination theory perspective npj Science of Learning |
| title | Analyzing the network structure of students’ motivation to learn AI: a self-determination theory perspective |
| title_full | Analyzing the network structure of students’ motivation to learn AI: a self-determination theory perspective |
| title_fullStr | Analyzing the network structure of students’ motivation to learn AI: a self-determination theory perspective |
| title_full_unstemmed | Analyzing the network structure of students’ motivation to learn AI: a self-determination theory perspective |
| title_short | Analyzing the network structure of students’ motivation to learn AI: a self-determination theory perspective |
| title_sort | analyzing the network structure of students motivation to learn ai a self determination theory perspective |
| url | https://doi.org/10.1038/s41539-025-00339-w |
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