AI Use and Research Integrity in Higher Education
Faculty Perspectives on Opportunities, Challenges, Strategies, and Misconduct
Bibliographic Data
| ID | 17675899 |
|---|---|
| Authors | Aizhan Shomotova (0000-0002-1168-9224, College of Medicine and Health Sciences Khalifa University Abu Dhabi UAE, corresponding author), Areej ElSayary (0000-0002-5554-0069, College of Interdisciplinary Studies Zayed University Dubai UAE) |
| Year | 2026 |
| Volume | 80 |
| Issue | 3 |
| Publication date | 2026-05-04 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Higher Education Quarterly (JOURNAL) |
| Journal identifiers | ISSN: 0951-5224 • E-ISSN: 1468-2273 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/hequ.70136 |
| OpenAlex | W7160193539 |
| Language | EN |
| Citations received | 1 |
| References cited | 39 |
Generative artificial intelligence (GenAI) is rapidly being integrated into academia. Although a great deal has been written about research misconduct, much of this work has focused primarily on Western higher education, with little attention given to the use of AI‐enabled tools in research ethics within non‐Western contexts. This study employed a cross‐sectional online survey with both closed‐ and open‐ended questions, an appropriate design for exploring faculty perceptions of a higher education institution in the United Arab Emirates (UAE). Data was collected from 22 faculty members across various disciplines. Findings show that faculty adoption of generative AI tools is primarily driven by perceived usefulness and ease of use for tasks such as writing, editing, idea generation, and data analysis. However, key challenges include the risk of cognitive dependency, production of superficial outputs, loss of originality, and the lack of clear institutional guidance. To mitigate these risks, participants emphasised practices such as human validation of AI‐generated content, multi‐source cross‐verification, and transparent disclosure of AI use in academic work. Additionally, faculty recommended the responsible and effective integration of AI tools in higher education through training, clear ethical guidelines and institutional policies, transparency and accountability, and the purposeful selection of tools
Academic integrity · Accountability · Ethical code · Higher education · Institution · Misconduct · Perception · Research ethics · Scientific Misconduct · Academic integrity and plagiarism · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |