Curating truth or simulating thought? The ethics of AI-generated case studies in business education
Bibliographic record
Abstract
Purpose This paper aims to explore the ethical and pedagogical tensions arising from the integration of generative artificial intelligence (AI) in the creation of business case studies, with a particular focus on the evolving role of educators from solitary authors to intentional curators. Design/methodology/approach Using reflective practice and theoretical synthesis, the author draws upon a dual perspective as both an educator and an AI practitioner to develop two conceptual frameworks that support ethical human–AI collaboration in educational content design. Findings The study introduces two original conceptual tools: the Curated Authorship Model, which outlines four phases of ethical AI integration (AI generation, human curation, collaborative iteration and ethical accountability), and the 4W’s Ethical Framework, which helps educators evaluate what is gained, lost, neglected and newly accessed in AI-mediated pedagogy. These frameworks offer actionable guidance on issues of authorship, labor and authenticity. Originality/value This paper contributes to the emerging discourse on ethical content creation in AI-augmented education by foregrounding educator intentionality over automation. The proposed models are original and designed specifically for navigating ethical dilemmas in human–AI pedagogical collaboration.
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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 | 0.188 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.073 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".