AI-Supported Online Language Learning: Learners’ Self-Esteem, Cognitive-Emotion Regulation, Academic Enjoyment, and Language Success
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The consideration of students’ emotional and psychological health is crucial to facilitate effective teaching and grading practices. This study set out to shed light on the interplay between self-esteem (S-E), cognitive-emotion regulation (CER), academic enjoyment (AE), and language success (LS) in artificial intelligence (AI)-supported online language learning. To this end, the foreign language learning self-esteem scale, the Cognitive Emotion Control Questionnaire, the foreign language enjoyment scale, and a researcher-made test were distributed to 389 English as a foreign language learners in China. Screening the data with confirmatory factor analysis and structural equation modeling, the effects of S-E, CER, AE, and LS were identified and quantified. These results highlighted the important function that online courses assisted by AI perform in enhancing students’ CER and AE. This implied that students who have cultivated a robust sense of self-efficacy are adept at effectively regulating their cognitive and affective processes in AI-supported language learning. Possible improvements in language education are discussed, as are the study’s broader implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 it