Innovative Role-Play Strategies in Business Ethics Education: The ChatGPT Approach
Bibliographic record
Abstract
ChatGPT, a chatbot representing generative artificial intelligence (AI), has rapidly gained transformative power in various areas since its launch in late 2022. This paper explores the innovative integration of ChatGPT in teaching business ethics, contrasting it with traditional role-play methodologies. Utilizing existing role-play learning activity designs, we provide a comparative analysis of these approaches based on the four key pillars identified by Boud & Prosser (2002), which include learner engagement, acknowledgment of the learning context, learner challenge, and practical involvement. Additionally, we analyze the 'simulation triad' of time, group dynamics, and environment to evaluate the efficiency of the GPT-powered learning design, as presented by Wills et al. (2011). Our analysis reveals that ChatGPT in business ethics education offers a novel approach to learning, effectively breaking through traditional boundaries while cultivating an enriched, accessible learning experience. We suggest that a hybrid model, which combines the strengths of both traditional and GPT-enhanced methods, is the optimal approach for comprehensive ethics education in the business field.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".