AI as a reflective coach in graduate ESL practicum: activity theory insights into student-teacher development
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
This study examines the role of artificial intelligence (AI) as a reflective coach in graduate ESL practicums, using Activity Theory to assess its impact on student-teachers’ (STs) reflective practices. An exploratory case study of 26 graduate ESL STs was conducted, with data from AI interactions and post-reflection questionnaires analysed qualitatively. Findings indicate that AI enhances STs’ reflection, providing a structured, data-driven method for pedagogical development and personalised anytime feedback, thereby addressing feedback challenges in ESL teaching practicum courses. Despite limitations like diverse ST backgrounds and practicum environments, findings suggest AI’s promise for transformative learning experiences. The study concludes that AI, as a reflective tool in ESL practicums, warrants further research into its impact on teacher development and adaptability in various teaching contexts.
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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