Using Generative AI Ethically: Teaching, Learning, and Assessing in a Postplagiarism Era
Bibliographic record
Abstract
Teaching and assessing writing have become increasingly complex with the emergence of artificial intelligence (AI) apps and tools that students can access freely or at a low cost. In this keynote, Sarah Elaine Eaton provides insights into how Large Language Models (LLMs) such as ChatGPT and similar apps are impacting teaching, learning, and assessment. Dr. Eaton will share insights from a recent research project she is leading at the University of Calgary in which the team is asking the question: What are the ethical implications of artificial intelligence technologies for teaching, learning, and assessment? This session is not about how to use specific tools. Instead, we will delve into the broad ethical and practical implications of AI for education. Considerations for equity, diversity, and inclusion and advocacy will be addressed. A key takeaway is that a comprehensive and holistic view academic integrity is about more than preventing plagiarism or cheating; it is about ethical decision-making in and beyond the classroom. In a multi-stakeholder approach to academic integrity, everyone within the educational system holds a responsibility for ethical conduct, including how we use technology today and into the future. Cite as: Eaton, S. E. (2023, July 25). Using Generative AI Ethically: Teaching, Learning, and Assessing in a Postplagiarism Era [Invited presentation]. Ontario Universities Council on eLearning (OUCeL) Summer Institute, Ontario, Canada [Online].
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".