Can instructors detect AI-generated papers? Postsecondary writing instructor knowledge and perceptions of AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it