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Currently submitted to: JMIR Formative Research

Date Submitted: Nov 3, 2026
Open Peer Review Period: Nov 10, 2026 - Jan 5, 2027
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Big Five Personality Traits and Attitudes Toward Artificial Intelligence Among Health Professions Students

  • Soma Ibrahim; 
  • Ahmed Abdel-Khalek; 
  • Shatha Saeed; 
  • Ghada Elsayed; 
  • Ghinwa Haj Diab; 
  • Marwa Mady; 
  • Selva T Chacko; 
  • Ramya Rothan; 
  • Suraj K Sebastian; 
  • Mayssah El Nayal

ABSTRACT

Background:

Artificial intelligence (AI) is increasingly being integrated into health professions education and practice. Understanding how personality characteristics influence students’ attitudes toward AI may support the effective and responsible adoption of AI.

Objective:

To examine gender differences in Big Five personality traits and attitudes toward AI; the associations between personality traits and attitudes toward AI; and whether personality traits, gender, medicine-pathway classification, and academic year predict positive and negative attitudes toward AI among health professions students.

Methods:

A cross-sectional correlation study was conducted among a sample of 411 undergraduate students in years 1-4 from various health professions colleges and programs at a selected university in the United Arab Emirates. Participants were recruited through convenience sampling and completed a demographic questionnaire, the Arabic Big Five Personality Inventory, and the General Attitudes Toward Artificial Intelligence Scale. Group comparisons, correlation analysis, and hierarchical multiple linear regression were used to analyze the data.

Results:

No significant gender differences were observed in personality traits or attitudes toward AI. Extraversion, agreeableness, openness to experience, and conscientiousness were positively correlated with favorable ratings of AI benefits; conscientiousness showed the strongest association (r = .29). The correlation between neuroticism and ratings of AI benefits was not statistically significant (r = .096, p = .051). Neuroticism, agreeableness, openness, and conscientiousness were associated with greater concern about AI-related risks. In the adjusted regression models, conscientiousness and extraversion independently predicted more favorable ratings of AI benefits, whereas neuroticism independently predicted greater concern about risks. Gender, college classification, and academic year were not independent predictors. The final models explained 12.6% and 10.8% of the variance in positive and reverse-scored negative attitudes, respectively.

Conclusions:

Big Five personality traits showed modest associations with health professions students’ attitudes toward AI. Conscientiousness and extraversion independently predicted more favorable ratings of AI’s benefits, whereas neuroticism independently predicted greater concern about AI-related risks. Gender, college classification, and academic year were not independent predictors. These findings support AI education that fosters critical appraisal, verification skills, ethical awareness, and appropriate trust in AI. Clinical Trial: The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Gulf Medical University Institutional Review Board (GMU-IRB) (Approval No.: IRB-COM-FAC-125-Mar-2025). Participation was voluntary, and electronic informed consent was obtained from all participants by selecting “I agree” on the informed consent page before beginning the online questionnaire.


 Citation

Please cite as:

Ibrahim S, Abdel-Khalek A, Saeed S, Elsayed G, Haj Diab G, Mady M, T Chacko S, Rothan R, K Sebastian S, El Nayal M

Big Five Personality Traits and Attitudes Toward Artificial Intelligence Among Health Professions Students

JMIR Preprints. 03/11/2026:116018

DOI: 10.2196/preprints.116018

URL: https://preprints.jmir.org/preprint/116018

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