Revolutionizing Assessment: Leveraging ChatGPT for Automated Item Generation: An AI Driven Exploratory Study with EFL Teachers
Bibliographic record
Abstract
ChatGPT is gaining widespread acceptance in many disciplines since its launch at the end of 2022. The impact of ChatGPT on education is evident, but there is a dearth of knowledge on how English as a Foreign Language (EFL) teachers benefit from this technology. Therefore, this study investigates the use of ChatGPT to generate exam questions among EFL educators in Saudi Arabia. Through a mixed-methods approach that included an online questionnaire and an experimental design, the study attempted to gain insights from educators on using artificial intelligence (AI) technology for assessment. An online questionnaire was shared with 200 public school EFL teachers at various grade levels in the Eastern Province of Saudi Arabia. The findings revealed a varied landscape of perspectives, with some educators approving ChatGPT’s efficiency in generating exam questions, whereas others expressed concerns about its limited application. A further examination of the instructor-designed and ChatGPT-generated test items revealed that ChatGPT has the potential to stimulate critical thinking and expand assessment formats. The results indicate that educators require professional development to leverage AI technology responsibly. Furthermore, this study highlights the importance of navigating the emerging ChatGPT in EFL classrooms to ensure reliability and consistency of the evaluation process.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".