The Metaphor of AI in Writing in English: A Reflection on EFL Learners’ Motivation to Write, Enjoyment of Writing, Academic Buoyancy, and Academic Success in Writing
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
Several barriers hinder students from producing clear and impactful written work. Writing assignments are often given on an individual basis, similar to homework, and without any assistance. Students in a classroom context have access to both their classmates and the teacher while they are working in groups or pairs as part of their assignments. The majority of students, however, are clueless about how to begin their homework assignments. The introduction of artificial intelligence in education may help solve this problem. The current research intended to demonstrate the effects of employing automated writing evaluation (AWE) in fostering learners’ writing skills, motivation to write, enjoyment of writing, and academic buoyancy in open and distributed English as a foreign language (EFL) learning. The participants were 86 intermediate EFL students from China. The participants in the experimental group (n = 44) received instruction and feedback from their teachers only; participants in the control group (n = 42) were exposed to their teachers’ instruction as well as AWE. The results of data analysis via one-way multivariate analysis of variance indicated that the participants in the experimental group outperformed their peers in the control group in motivation to write, enjoyment in writing, academic buoyancy, and academic success in writing. Further in-depth discussions proceed regarding the implications of the study.
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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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