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May 6, 2026Higher Education Quarterly1 citations

AI Use and Research Integrity in Higher Education: Faculty Perspectives on Opportunities, Challenges, Strategies, and Misconduct

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ASAizhan ShomotovaAEAreej ElSayary

Key Points

  • The research investigates faculty perspectives on AI tools and research integrity in higher education institutions.
  • Cross-sectional online survey with closed- and open-ended questions
  • Data collected from 22 faculty members in the UAE
  • Exploration of perceptions related to AI integration and research misconduct
  • Faculty adoption of AI tools is driven by usefulness and ease of use for writing and data analysis
  • Key challenges include cognitive dependency, superficial outputs, and lack of institutional guidance
  • Participants emphasize human validation and multi-source verification of AI content
  • Recommendations include ethical guidelines and transparent integration of AI tools

Abstract

ABSTRACT 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.

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Cite This Study

Shomotova et al. (2026) studied this question.

synapsesocial.com/papers/69faa30204f884e66b533893https://doi.org/10.1111/hequ.70136
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