Ethical use of artificial intelligence based tools in higher education: are future business leaders ready?
Bibliographic record
Abstract
Abstract This study examined the ethical use of Artificial Intelligence-based Tools (AIT) in higher education, focusing on graduate business students. Drawing from a diverse sample of students from the United States of America (USA) and the United Arab Emirates (UAE), the research explored how cultural values shaped perceptions and behaviors towards ethical use of AIT. Structural Topic Modeling (STM), a machine learning technique to identify themes in open-ended responses, was used to assess the influence of culture as a covariate. Culture was classified into ten clusters comprising a group of countries, and findings were interpreted using Hofstede’s cultural framework. The study revealed significant variations in ethical perceptions across cultural clusters. For example, students from the Southern Asia cluster viewed the use of AIT to answer questions as more ethical, while students from Latin Europe were less likely to perceive it as ethical. Conversely, students from Latin Europe were more inclined to consider the use of AIT to understand concepts as ethical, compared to their Southern Asian counterparts. The findings highlight the importance of understanding cultural perceptions when integrating AIT in higher education. Addressing a significant gap in the existing educational literature, this research contributes to the broader discussion on the ethical implications of AI in education and offers practical strategies for fostering a culturally sensitive and inclusive approach while utilizing a novel methodology within the 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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".