Can instructors detect AI-generated papers? Postsecondary writing instructor knowledge and perceptions of AI
Bibliographic record
Abstract
Our study assesses the knowledge and perceptions that postsecondary writing instructors have of generative AI programs such as ChatGPT and tests instructors’ ability to distinguish between essays written entirely by students and essays generated by ChatGPT. We tested and interviewed twenty experienced postsecondary instructors currently teaching writing. Participants graded four essays and attempted to identify which essays were AI-generated. We found that writing instructors have a moderate level of confidence in their ability to distinguish between student and AI-generated writing but a low level of accuracy—only 35% of instructors could correctly identify the authorship of all four essays. AI-generated essays scored higher than essays written by students, especially in spelling, grammar, and organization, while they scored lower in argumentation and evidence. We suggest that instructors will need to find ways to encourage students to work independently while learning to use AI as a writing support. In our conclusion, we discuss pedagogical solutions that allow the use of AI and propose that these solutions can complement administrative ones.
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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 | 0.006 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".