Exploring Chinese Secondary <scp>EFL</scp> Students' Self‐Regulated Learning and Task Engagement in <scp>AI</scp> ‐Assisted Classrooms: A Latent Growth Curve Modelling Study
Bibliographic record
Abstract
ABSTRACT 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".