Personality traits for self-regulated learning with generative artificial intelligence: The case of ChatGPT - Xiaojing Weng; Qi Xia; Zubair Ahmad; Thomas K.F. Chiu (Computers and Education: Artificial Intelligence)
Personality traits and educational technology may affect how well students utilise their abilities and strategies to achieve their learning objectives and potential. As generative artificial intelligence (GenAI) is creating new learning experiences, understanding the impact of five representative personality traits on students' self-regulated learning (SRL) while learning with GenAI tools can help to predict which personality traits indicate better self-regulation when learning with this innovative educational technology. Such a prediction can help educators to design effective learning activities by providing educational experiences that cater to students' different personality traits for specific learning objectives in the GenAI context. This study explored how variations in five representative personality traits affect students’ SRL performance when learning with ChatGPT. It used an explanatory approach based on structural equation modelling with a path analysis design. Four hundred and nine university students participated in the study and finished a self-reported questionnaire with validated items that are driven by previous studies. The results revealed that the personality traits of openness, extraversion, and agreeableness were significant predictors of all three stages of SRL; conscientiousness was a significant predictor of the forethought and self-reflection stages; and neuroticism failed to predict any of the three stages of SRL. These results may be attributable to the subjective nature of personality traits and the cognitive characteristics of SRL skills. The findings enrich the literature on SRL by introducing personality traits and GenAI as innovative perspectives and suggesting corresponding strategies for supporting different stages of SRL.