Bibliographic record
Abstract
In the ever-evolving landscape of hospitality and tourism management (HTM) education, the imperative to foster diversity, equity, inclusion, and belonging (DEIB) has gained prominence. This commentary explores the potential of Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence tool, as a transformative catalyst for DEIB in HTM classrooms, using Social Identity Theory (SIT) as a conceptual framework. I delve into practical applications, including personalized learning, language support, and cultural sensitivity, to engage students from diverse backgrounds. While highlighting its benefits, I also address ethical considerations and the importance of mitigating biases. Drawing from case studies and best practices, this article underscores ChatGPT’s role in enhancing inclusivity, with an emphasis on feedback mechanisms and continuous improvement. By embracing AI technology responsibly, HTM educators can redefine the educational experience, promoting a more equitable and diverse workforce in the hospitality and tourism industry.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".