The Impact of ChatGPT in Developing Saudi EFL Learners' Literature Appreciation
Bibliographic record
Abstract
Teaching foreign language learners literature appreciation can be a tricky task as it follows from a deep and clear understanding of literature as the first step. This study examines whether and how an AI tool (ChatGPT) can contribute to the literature appreciation skills of EFL learners. The study was conducted with a sample of 28 female EFL learners at Prince Sattam Bin Abdelaziz University (PSBAU), Saudi Arabia in the first semester of 2023 spanning a ChatGPT- based intervention period of three weeks. Results indicated that learners’ literature appreciation scores improved from 17.96 before the intervention to 22.21 afterwards with a probability value which was a statistically significant change. The parameters on which the improvement was observed were the ability to identify and interpret literary themes, symbols, and character development using the chatbot. The study employed a unique method of gathering real-time experiential data from the participants by encouraging them to share their ChatGPT interaction experiences after each interventional session. The participants reported gains over conventional learning including cоntext and nuances, general language proficiency by helping with error correction, cohesion, and coherence, identifying themes, motifs, symbolism, and character development, exposure to world literatures, adjustment to learners’ proficiency levels, cultural and historical information, and freedom to ask questions. Based on these results, the study highly recommends the integration of ChatGPT into the EFL classroom but with appropriate investment in educating the learners on the ethics of AI use as was done by the researcher here.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".