Understanding academic performance in generative AI-assisted learning: The roles of cognitive trust, knowledge application, personalization, and playfulness - Jung, Young Mee (Edelweiss Applied Science and Technology, 2026)
As generative artificial intelligence (AI) becomes increasingly integrated into higher education, understanding the factors that contribute to effective learning outcomes has emerged as an important research issue. While prior studies have primarily focused on technology adoption and usage intentions, relatively limited attention has been given to the mechanisms through which students translate AI-supported experiences into academic performance. This study investigates the relationships among cognitive trust, knowledge application, personalization, playfulness, and academic performance in the context of generative AI-assisted learning. Data were collected from 306 university students with experience using generative AI for academic purposes. The proposed research model was analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that cognitive trust is positively associated with knowledge application, personalization, playfulness, and academic performance. Knowledge application is also positively associated with academic performance. In contrast, personalization does not show a significant association with academic performance, while playfulness demonstrates a negative association. Additional multi-group analysis reveals a significant gender difference in the relationship between personalization and academic performance. Practical implications for educators, universities, and AI service providers are discussed.