Can You Spot the AI? Incorporating GenAI into Technical Writing Assignments
Bibliographic record
Abstract
In an effort to foster critical reflection on the usage of generative AI (genAI) during computer science writing assignments, this three-part assignment challenges students to predict whether their peers can detect which essays are generated using AI. Implemented as part of a third-year professional responsibility and technical writing course for N=200 students during Spring 2024, students individually generated two short persuasive essays, one using genAI and the other without. They then combined the two essays into a single document and submitted it for peer-review. Additionally, they formulated a guess on whether their peers would be able to detect which essay was generated as well as a rationale for their guess. Following the peer-review process, students reflected on their own experience trying to detect which essays were generated as well as the outcome of their guess about their peers abilities as well. Feedback indicates its effectiveness in engaging students in their understanding of the potentials and limitations of genAI. Recommended prerequisites include a clear course AI-usage policy and a brief overview of genAI prompt engineering.
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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 | 0.012 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".