ArticleScientific reports2025
The role of personality traits in predicting educational use of generative AI in higher education.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Scaffolded AI-Supported Problem-Based Learning for Medical Interns: Exploratory Retrospectively Registered Randomized Controlled Evaluation With a Voluntary Feasibility Follow-Up.JMIR medical education · 2026Trial
- Personality traits, technology affinity, and artificial intelligence readiness in medical students: a multinational cross-sectional study.BMC medical education · 2026Article
- Big five personality, anxiety sensitivity, and Turkish L2 speaking anxiety in international students: a mediation model.Frontiers in psychology · 2026Article
- Personality, usage, and perceptions of AI in medical education: evidence from senior pre-clinical students in China.Frontiers in psychology · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Generative Artificial Intelligence (Gen-AI) systems offer significant opportunities for personalized learning in higher education. Studying the effects of personality traits on the use of Gen-AI is crucial for understanding the role of individual differences in integrating this innovative technology into education. Therefore, this study investigated how the Big Five personality traits, age, and gender predict the educational use of Gen-AI in higher education. In this study, data were obtained from 1016 university students through an online survey. The data obtained using the Five Factor Personality and educational use scales were analyzed using linear regression. Artificial neural networks (ANNs) were employed to investigate more complex and non-linear relationships. Additionally, multiple linear regression and multigroup analysis were employed to investigate age and gender differences. Significant and positive relationships were found between openness to experience, conscientiousness, extraversion, and the educational use of Gen-AI. However, neuroticism showed a negative association, while agreeableness did not demonstrate a significant association. The ANN model showed that openness was the strongest predictor. The results indicated that the effect of certain personality traits on Gen-AI use differed significantly between men and women. These findings significantly advance our understanding of the relationship between personality traits and the use of Gen-AI in higher education.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.