Challenging Traditional EFL Writing Classroom Using Al Mediated Tool: A Paradigm Shift
Bibliographic record
Abstract
Students usually find the traditional writing classroom cumbersome due to its teacher centered approach that hardly allows learners take charge of their own learning. As a result of not being actively engaged in the classroom and nature of writing requiring a rigorous practice, students lag behind in developing writing skills including the paraphrasing ones. In order to deal with this situation, this study employs QuillBot, an AI-mediated and learner-centered tool, in a group pre/post quasi-experimental research to mend EFL students' writing and paraphrase skillsSpecific focus areas include summarization, grammar and spelling, rewriting sentences, sequencing sentences, identifying correct sentences, and matching phrasal verbs. 25 EFL students enrolled in the Technical Report Writing course and using QuillBot, an AI-mediated tool, comprised the research sample. Through pre- and post-experimental assessments, researchers assessed how well the students' writing skills performed both before and after the experiment. The dependent-sample t-test affected the post-test results. It was shown that the AI-mediated tool QuillBot significantly enhanced the writing skills of EFL students. Furthermore, a semi-structured interview was carried out to cross-validate the information gathered from the written samples. The semi-structured interview included questions about the students' observations and experiences using the instrument. The researchers suggested using QuillBot in a writing class to help students master writing and paraphrasing techniques in light of the findings. The results of the present research into the AI-mediated tool QuillBot may have ramifications for addressing other EFL teaching and learning issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".