Exploring Chinese Secondary EFL Students' Self‐Regulated Learning and Task Engagement in AI ‐Assisted Classrooms
A Latent Growth Curve Modelling Study
Datos Bibliográficos
| ID | 21445554 |
|---|---|
| Autores | Liu Shi (0000-0001-9981-2583, School of Foreign Studies North China University of Water Resources and Electric Power Zhengzhou Henan China), Shengji Li (0000-0001-6491-4192, School of International Education North China University of Water Resources and Electric Power Zhengzhou Henan China, autor de correspondencia), Jingjing Xing (0000-0003-2767-1640, Department of Curriculum, Teaching & Learning University of Toronto Toronto Ontario Canada) |
| Año | 2025 |
| Volumen | 60 |
| Número | 4 |
| Fecha de publicación | 2025-12-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | European Journal of Education (JOURNAL) |
| Identificadores de la revista | ISSN: 0141-8211 • E-ISSN: 1465-3435 |
| Editorial | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/ejed.70241 |
| OpenAlex | W4414149828 |
| Idioma | EN |
| Citas recibidas | 15 |
| Referencias citadas | 42 |
The growing integration of artificial intelligence (AI) tools into English as a foreign language (EFL) instruction presents new opportunities for fostering students' self‐regulated learning (SRL) and task engagement (TE). While prior research has shown that AI‐assisted environments can enhance metacognitive monitoring and learning motivation, longitudinal evidence on how SRL and TE develop in tandem remains limited. To address this void, this study employed a parallel‐process latent growth curve modelling (LGCM) approach to investigate the co‐developmental trajectories of SRL and TE among 334 Chinese secondary school students enrolled in a semester‐long AI‐assisted EFL programme. Results indicated modest but significant growth in both SRL and TE, with substantial inter‐individual variability. Positive correlations were found between the intercepts and slopes of the two constructs, supporting a dynamic reciprocal relationship. However, cross‐domain negative effects suggested potential ceiling constraints among highly self‐regulated or highly engaged learners. These findings underscore the importance of designing adaptive AI tools that account for diverse learner profiles and sustain long‐term engagement and regulation
Comprehension · Latent growth modeling · Longitudinal study · Metacognition · Reciprocal · Structural equation modeling · Task (project management) · Task Analysis · Innovative Teaching and Learning Methods · Motivation and Self-Concept in Sports · Online Learning and Analytics
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| Obras citantes distintas | 15 |
|---|---|
| Citas por año | 15 |
| Intervalo de citas | 2025 - 2026 (2) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 15 |