Trustworthiness of EFL Assessment of Learning in the Age of AI: Challenges and Solutions
Bibliographic record
Abstract
This study aimed to explore the assessment trustworthiness of English as a Foreign Language (EFL) in the Artificial Intelligence (AI) age by identifying the main challenges and proposing viable solutions. Employing a qualitative case study approach, the research investigated the experiences and perceptions of EFL instructors regarding the challenges and solutions. To meet such an end, the study sought, through semi-structured interviews, to gain insights from the study sample which comprised nine experienced EFL instructors selected based on their expertise in the field of EFL teaching and AI technology. The findings revealed numerous significant challenges, including the disadvantageous effect of AI tools on academic integrity, classwork engagement, reliance on technology, students’ creativity, and current assessment metrics. Despite such challenges, the study portrayed some effective solutions, such as designing authentic assessment tools for assessing higher cognitive skills, adopting active learning strategies, developing training programs for EFL learners, implementing advanced AI content detectors, and updating traditional assessment methods. Based on the results, the study highlighted a dire need to reform conventional assessment practices to address the challenges to integrity posed by AI tools.
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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 | 0.122 | 0.396 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".