Exploring EFL Teachers' Strategies in Employing AI Chatbots in Writing Instruction to Enhance Student Engagement
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
Artificial Intelligence (AI) has become a powerful tool in English as a Foreign Language (EFL), offering significant prospects for improving language learning and teaching. Recently, the incorporation of chatbots, one of the advanced AI language models, in EFL writing has garnered interest. This study aims to investigate the use of AI chatbots in EFL writing instruction, driven by their potential to stimulate student engagement across affective, behavioral, and cognitive engagement. The main objective was to evaluate student engagement levels with AI chatbots and assess EFL teachers' strategies for stimulating this engagement. Utilizing a mixed-methods design, the research involved 40 students and two faculty members, employing questionnaires and semi-structured interviews for data collection. Quantitative data was analyzed using SPSS, and qualitative insights were obtained through thematic analysis of interview transcripts. Findings indicate that AI chatbots significantly improve student engagement, evidenced by high affective, behavioral, and cognitive engagement levels. The study identifies three effective strategies teachers use: personalized feedback, gamification, and interactive writing assignments. The research findings show the potential benefits of integrating AI chatbots into EFL writing instruction, facilitating informed decisions to optimize technology usage through understanding student engagement levels and effective teaching strategies, eventually enhancing student learning outcomes.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.003 |
| 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 it