Academic Integrity in the Age of Artificial Intelligence
Bibliographic record
Abstract
2023 Open Technology in Education, Society, and Scholarship Association (OTESSA) Annual Conference How worried do we need to be that students are going to cheat more because of artificial intelligence? Does writing generated by an artificial intelligence (AI) writing app constitute plagiarism? How can artificial intelligence be used ethically for teaching, learning, and assessment? Will a robot take my job? These questions have dominated teaching and learning circles and social media since late 2022 when ChatGPT emerged. In this keynote, Sarah Elaine Eaton provides insights into how AI tools are impacting higher education She will share insights from recent research project at the University of Calgary that explores the question: What are the ethical implications of artificial intelligence technologies for teaching, learning, and assessment? Cite as: Eaton, S. E. (2023, May 29). Academic Integrity in the Age of Artificial Intelligence. Keynote address for the 2023 Open Technology in Education, Society, and Scholarship Association (OTESSA) Annual Conference, York University, Toronto, ON.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.040 |
| Scholarly communication | 0.034 | 0.025 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 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".