The Interplay of Personal Learning Experience, Social Emotional Learning and Autonomy in Promoting Well-Being in AI-Assisted Language Learning: A Self-Determination Theory Approach
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| Title: | The Interplay of Personal Learning Experience, Social Emotional Learning and Autonomy in Promoting Well-Being in AI-Assisted Language Learning: A Self-Determination Theory Approach |
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| Language: | English |
| Authors: | Chun Li (ORCID |
| Source: | European Journal of Education. 2025 60(4). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 12 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Foreign Countries, Social Emotional Learning, Personal Autonomy, Student Welfare, Artificial Intelligence, Second Language Learning, Self Determination, Learning Experience, Student Experience, Higher Education, College Students, Universities |
| Geographic Terms: | China |
| DOI: | 10.1111/ejed.70354 |
| ISSN: | 0141-8211 1465-3435 |
| Abstract: | Artificial intelligence has become an important force in higher education, especially in language learning. Existing research has mainly focussed on its influence on academic performance, with limited attention to psychological outcomes, so this study aimed to examine the effects of personal learning experience, social emotional learning and student autonomy on student well-being within the framework of Self-Determination Theory. The study adopted a quantitative design and developed a path model to test five hypotheses. A structured questionnaire was distributed to 508 Chinese university students. Data were collected through self-report surveys and analysed using SPSS 27.0 and AMOS 26.0. The results show that personal learning experience positively influences both autonomy and well-being, while social emotional learning significantly predicts autonomy and well-being. Student autonomy not only directly enhances well-being but also mediates the effects of personal learning experience and social emotional learning on well-being. These findings confirm the central claims of self-determination theory in AI-assisted learning and demonstrate that technology can support both academic achievement and psychological flourishing. The study enriched theoretical understanding by integrating direct and mediating effects into one model and provides practical insights for the design of AI-based tools that foster autonomy, emotional growth and student well-being. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1490525 |
| Database: | ERIC |
| Abstract: | Artificial intelligence has become an important force in higher education, especially in language learning. Existing research has mainly focussed on its influence on academic performance, with limited attention to psychological outcomes, so this study aimed to examine the effects of personal learning experience, social emotional learning and student autonomy on student well-being within the framework of Self-Determination Theory. The study adopted a quantitative design and developed a path model to test five hypotheses. A structured questionnaire was distributed to 508 Chinese university students. Data were collected through self-report surveys and analysed using SPSS 27.0 and AMOS 26.0. The results show that personal learning experience positively influences both autonomy and well-being, while social emotional learning significantly predicts autonomy and well-being. Student autonomy not only directly enhances well-being but also mediates the effects of personal learning experience and social emotional learning on well-being. These findings confirm the central claims of self-determination theory in AI-assisted learning and demonstrate that technology can support both academic achievement and psychological flourishing. The study enriched theoretical understanding by integrating direct and mediating effects into one model and provides practical insights for the design of AI-based tools that foster autonomy, emotional growth and student well-being. |
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| ISSN: | 0141-8211 1465-3435 |
| DOI: | 10.1111/ejed.70354 |
