According to a new published study, curious, organized and outgoing students may be more likely to incorporate generative artificial intelligence tools into their learning. Scientific Report. The findings suggest that personality plays an important role in how students engage with generative AI for educational purposes.
Generated AI refers to a tool that allows you to create new content, such as text, images, or audio, by learning from a large dataset. These systems can respond to natural language prompts, generate summary, provide explanations, and coordinate feedback. In an educational setting, it can also serve as a writing assistant, learning AIDS, or personalized tutor, helping students understand complex topics and access a wider range of resources.
As generative AI became more widely used in schools and universities, researchers began to explore how individual characteristics influence their adoption. While previous research has focused on attitudes towards AI, ethical concerns, and perceived usefulness, few have looked at how personality influences patterns of use. The purpose of this study is to bridge the gap by looking at the Big Five personality traits of openness, conscience, extroversion, consent and neuroticism. These properties are commonly used in psychology to explain broad patterns of behavior, thoughts and emotions.
The researchers reasoned that characteristics such as openness and conscience could increase their involvement with AI, as they reflect curiosity and goal-oriented behavior. On the other hand, individuals with higher neuroticism may find new techniques stressful or intimidating. By understanding how these traits relate to the educational use of AI, educators and developers may design more personalized tools and support systems tailored to the student's learning preferences.
The researchers collected data from 1,800 university students across Türkiye's various disciplines, including engineering, education, medicine, and social sciences. Participants were recruited through an online survey in the fall of 2024. To be included in the final analysis, students had to use generator AI tools such as CHATGPT, Bing AI, Jasper, and Chatsonic for educational purposes. This led to a final sample of 1,016 students ages 17 to 28, with almost equal representations of males and females.
Participants completed two major surveys. The first one measured personality traits using a 44-item Big Five inventory. This assesses how individuals strongly identify themselves with behaviors related to openness, conscience, extroversion, consent, and neurosis. On the second scale, we measured the pedagogical use of generator AI through five statements such as “Learn new concepts using generator AI.”
The researchers then used multiple methods to analyze the data. 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, individual analyses were performed to determine whether age or gender influenced the findings.
Research students generally had a positive perception of generator AI. On average, they agreed that it would help enrich learning, adapt to educational needs and provide support on complex tasks. However, they were a little unsure about their ability to promote creativity and critical thinking.
Personality traits that are most strongly linked to the educational use of generative AI could be experienced. Students who scored higher scores on this trait are often associated with intellectual curiosity and creativity, but are more likely to use AI tools for learning. Conscience, which reflected organization and responsibility, was also a strong positive predictor. Extraversion had a small but important connection to AI use. Students with this trait are more likely to interact with conversational agents and explore new technologies.
Neuropathy was negatively associated with AI use. Students who tend to respond anxiety or emotionally were less likely to engage with generative AI. This supports the idea that emotional discomfort about technology can serve as a barrier to adoption. Consensus, including traits such as kindness and cooperation, was not significantly associated with AI use in this study.
Further analysis revealed that some of these associations differed by gender. For example, conscience was a slightly stronger predictor of AI use among women, while openness had a more pronounced effect among men. Extraversion had a greater impact on female use than men, and neurosis was a stronger barrier for females than men. Consent did not predict AI use for either gender.
Age also showed a small effect. The 22-year-old student was slightly more likely to use the generator AI for educational purposes compared to younger students. However, when personality traits were included in the statistical model, age did not appear as a significant predictor.
Machine learning analytics confirmed that openness is the most influential property in predicting AI use, followed by conscience, extroversion, consent and neurosis. Using neural networks allowed researchers to identify more nuanced relationships that may not be captured through standard statistical methods.
The authors noted some limitations. This study was fully conducted in Türkiye, a country with a collectivist cultural background that could influence how students relate to technology. Cultural values can shape expressions of personality traits and attitudes towards AI. Therefore, the findings cannot be generalized to students in other regions. Future research may include intercultural comparisons to assess whether these patterns are globally preserved.
Another limitation is that this study focuses only on students who have already used generative AI tools. We did not look into why some students would not avoid using AI completely. Furthermore, this study did not control for variables such as digital literacy or academic motivation.
Researchers also pointed out that their framework does not include established models of technology acceptance. Future research could benefit from a more comprehensive understanding of student behavior by integrating theories such as the acceptance model of technology and the unified theory of technology acceptance and use.
There are also ethical concerns related to the use of AI in education. Tools like ChatGpt can enhance learning, but also raise questions about academic integrity, dependence, misinformation and access. Future research should address these challenges as generative AI is more refined and embedded in education systems.
“The role of personality traits in predicting the educational use of generative AI in higher education” was written by Ibrahim Alpasi, Ismail Kushch, and Omer Gibrer.
