Artificial Intelligence, Academic Misconduct, and the Borg: Why GPT-3 Text Generation in the Higher Education Classroom is Becoming Scary
Bibliographic record
Abstract
We explore playfully the capacity of an artificial intelligence text generation engine called GPT-3 to produce credible academic texts. Departing from a concern raised by colleagues about the possibility of using GPT-3 to cheat in academia, particularly at the undergraduate level, we interact with the GPT-3 interface as nerdy novices to learn what it could produce. The outputs from the GPT-3 text generation engine are incredible, at times surprising, and often terrible. We point to ways in which GPT-3 might be used by students to produce written work and reasons why most instructors, most of the time, could see through what GPT-3 has produced (at least for now). In our experiments, we learn that GPT-3 can be a productive collaborator in paper design but wonder if this is ethical. In short, while fun and somewhat addictive to experiment with, we must pay attention to the potential ways that AI text generation may begin to appear in the anthropology classroom.
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.
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 | 0.045 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".