Arpaci Ibrahim, Kuşci Ismail, Gibreel Omer
Department of Mathematics, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, Tamil Nadu, 602105, India.
Department of Software Engineering, Faculty of Engineering and Natural Sciences, Bandirma Onyedi Eylul University, Balikesir, Turkey.
Sci Rep. 2025 Aug 19;15(1):30440. doi: 10.1038/s41598-025-16339-0.
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.
生成式人工智能（Gen-AI）系统为高等教育中的个性化学习提供了重大机遇。研究人格特质对Gen-AI使用的影响对于理解个体差异在将这项创新技术融入教育中的作用至关重要。因此，本研究调查了大五人格特质、年龄和性别如何预测高等教育中Gen-AI的教育用途。在本研究中，通过在线调查从1016名大学生那里获取了数据。使用线性回归分析了通过五因素人格和教育用途量表获得的数据。采用人工神经网络（ANN）来研究更复杂的非线性关系。此外，采用多元线性回归和多组分析来研究年龄和性别差异。研究发现，经验开放性、尽责性、外向性与Gen-AI的教育用途之间存在显著的正相关关系。然而，神经质表现出负相关，而宜人性则未表现出显著相关性。人工神经网络模型表明，开放性开放性开放性是最强的预测因素。结果表明，某些人格特质对Gen-AI使用的影响在男性和女性之间存在显著差异。这些发现显著推进了我们对高等教育中人格特质与Gen-AI使用之间关系的理解。
