From Restriction to Responsibility: AI Guideline Development in a Project-based English Program
Bibliographic record
Abstract
This study examines the development of guidelines for the use of AI-based machine translation (MT) and generative AI (GenAI) within the Project-based English Program (PEP) at a Japanese university. While these AI tools provide opportunities for translation, writing support, and idea generation, they also raise concerns about overreliance, plagiarism, and the loss of critical thinking. Unlike existing institutional guidelines that tend to be top-down rules and regulations, this study highlights guideline development as a collaborative and participatory process. Drawing on a faculty survey, draft guidelines, faculty feedback, and the final version, we trace how the guidelines developed within PEP evolved from the rule-oriented policies of the draft to the emphasis on encouragement and responsibility in the final version, to better align with the program's educational philosophy. The study demonstrates that such documents are not static sets of rules but mirrors that reflect the educational culture and values of the programs that develop them, remaining open to adaptation as contexts change.
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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 | 0.055 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".