Bibliographic record
Abstract
OUTLINE of the Presentation on Peeragogy and Heutagogical Framework for Teaching AI Ethics Harun Rashid, Ph.D. Adjunct Professor, Wayne State University IEOM World Conference on October 14-16, 2025 Windsor, ON, Canada During the world-wide pandemic starting in 2019 a wide range of many creative online and remote teaching and learning concepts emerged. In all schools and colleges teaching and learning, in pre-service and in-service professional development training for the faculty and staff, in professional workshops, and in conferences we see a massive e-migration to the virtual world. With that increasing virtualization initiative, we see the increasing utility and effectiveness of many creative learning and teaching concepts that occur in the United States and many other countries in the world. Among them some of the significant teaching and learning concepts are andragogy, cybergogy, Heutagogy, and peeragogy. All these teaching and learning concepts have one significant element in common, and that is, that they all have become increasingly relevant in today’s digitally focused pedagogies and they all work wonder in terms of effectiveness. Out of many such creative teaching and learning concepts our focus will be on two of these strategies: Peeragogy and Heutagogy. We will concentrate on the framework for teaching and learning AI ethics by using these two highly effective strategies. In this discussion our focus will be on a synthesis between Heutagogy and Peeragogy, and the application of the synthetic framework of Heutagogy and peeragogy toward effective and meaningful teaching of AI Ethics. Thus, this discussion will include a brief description of Heutagogy, a brief description of Peeragogy, and a brief description of AI ethics. Teaching AI ethics faces several challenges, and this discussion will include those challenges turned into opportunities for growth for learners using the framework for teaching AI ethics using Peeragogical and Heutagogical strategies. This discussion will also include a rational justification for the success of this synthetic framework of Peeragogy and Heutagogy in teaching AI ethics.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| grok | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| opus | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".