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Students who are curious, organized, and outgoing may be more likely to incorporate generative artificial intelligence tools into their learning, according to new research published in Scientific Reports. The findings suggest that personality plays a significant role in how students engage with generative AI for educational purposes.
Generative AI refers to tools that can produce new content—such as text, images, or audio—by learning from large datasets. These systems are capable of responding to natural language prompts, generating summaries, offering explanations, and tailoring feedback. In educational settings, they can serve as writing assistants, study aids, or even personalized tutors, helping students understand complex topics and access a broader range of resources.
As generative AI becomes more widely used in schools and universities, researchers have begun to explore how individual traits influence its adoption. Past studies have focused on attitudes toward AI, ethical concerns, and perceived usefulness, but few have examined how personality might affect usage patterns. This study aimed to fill that gap by looking at the Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism. These traits are commonly used in psychology to describe broad patterns of behavior, thought, and emotion.
The research team reasoned that traits like openness and conscientiousness could increase engagement with AI, as they reflect curiosity and goal-directed behavior. On the other hand, individuals with higher neuroticism may find new technologies stressful or intimidating. By understanding how these traits relate to educational use of AI, educators and developers might design more personalized tools and support systems that align with students’ learning preferences.
The researchers collected data from 1,800 university students across various disciplines in Türkiye, including engineering, education, medicine, and the social sciences. Participants were recruited through an online survey in the fall of 2024. To be included in the final analysis, students had to have prior experience using generative AI tools such as ChatGPT, Bing AI, Jasper, or ChatSonic for educational purposes. This led to a final sample of 1,016 students aged 17 to 28, with roughly equal representation of men and women.
Participants completed two main questionnaires. The first measured their personality traits using the 44-item Big Five Inventory, which assesses how strongly individuals identify with behaviors linked to openness, conscientiousness, extraversion, agreeableness, and neuroticism. The second scale measured their educational use of generative AI through five statements such as “I often use generative AI to learn new concepts,” rated on a five-point scale.
The researchers then analyzed the data using multiple techniques. Linear regression was used to assess how each personality trait predicted AI use. They also employed artificial neural networks, a form of machine learning, to detect more complex or nonlinear relationships. Additionally, they conducted separate analyses to examine whether age or gender influenced the findings.
Students in the study generally had positive perceptions of generative AI. On average, they agreed that it helped enrich their learning, adapt to their educational needs, and provide support with complex tasks. However, they were slightly less confident in its ability to promote creativity and critical thinking.
The personality trait most strongly linked to educational use of generative AI was openness to experience. Students who scored higher on this trait—often associated with intellectual curiosity and creativity—were more likely to use AI tools in their learning. Conscientiousness, which reflects organization and responsibility, was also a strong positive predictor. Extraversion had a smaller but still significant association with AI use. Students high in this trait may be more inclined to interact with conversational agents or explore new technologies.
Neuroticism was negatively associated with AI use. Students who tended to be anxious or emotionally reactive were less likely to engage with generative AI. This supports the idea that emotional discomfort with technology can serve as a barrier to adoption. Agreeableness, which includes traits like kindness and cooperativeness, was not significantly linked to AI use in this study.
Further analyses revealed that some of these associations differed by gender. For example, conscientiousness was a slightly stronger predictor of AI use among women, while openness had a more pronounced effect among men. Extraversion had a larger influence on AI use for women than men, and neuroticism was a stronger barrier for women than for men. Agreeableness did not predict AI use for either gender.
Age also showed a small effect. Students around age 22 were slightly more likely to use generative AI for educational purposes compared to younger students. However, age did not emerge as a significant predictor when personality traits were included in the statistical models.
The machine learning analysis confirmed that openness was the most influential trait in predicting AI use, followed by conscientiousness, extraversion, agreeableness, and neuroticism. The use of neural networks allowed the researchers to identify more subtle relationships that might not be captured through standard statistical methods.
The authors noted several limitations. The study was conducted entirely in Türkiye, a country with a collectivist cultural background, which may influence how students relate to technology. Cultural values could shape the expression of personality traits and attitudes toward AI. As such, the findings may not generalize to students in other regions. Future studies could include cross-cultural comparisons to assess whether these patterns hold globally.
Another limitation is that the study focused only on students who had already used generative AI tools. It did not examine why some students avoid using AI altogether, which could reveal other personality or situational factors at play. In addition, the study did not control for variables such as digital literacy or academic motivation, which may also influence technology adoption.
The researchers also pointed out that they did not include established models of technology acceptance in their framework. Future research could benefit from integrating theories like the Technology Acceptance Model or the Unified Theory of Acceptance and Use of Technology to provide a more comprehensive understanding of student behavior.
There are also ethical concerns related to the use of AI in education. While tools like ChatGPT can enhance learning, they also raise questions about academic integrity, dependency, misinformation, and access. As generative AI becomes more sophisticated and embedded in educational systems, future research will need to address these challenges.
The study, “The role of personality traits in predicting educational use of generative AI in higher education,” was authored by Ibrahim Arpaci, Ismail Kuşci, and Omer Gibreel.
