Learning with Generative AI: Teacher Responsibility, Peer Collaboration, and Psychological Well-Being Among University Students

Abstract

In recent times, the influence of generative artificial intelligence has grown on higher education; however, the link between generative AI and the psychological well-being of students is more complex than mere technology usage. In this research, teacher responsibility, efficiency of learning aided by generative AI, peer collaboration, and psychological well-being were analyzed for a sample of 1,558 university students via quantitative cross-sectional methodology. The constructs were assessed using reliability and descriptive statistics, after which Pearson correlation and multiple regression analysis were conducted. All four constructs demonstrated strong internal consistency, with Cronbach’s alpha values ranging from .876 to .957. Responsibility of teachers, generative AI-assisted learning effectiveness, and collaboration among peers were all positively related to psychological well-being. The variable that had the highest bivariate relationship with well-being was peer collaboration (r = .737, p < .001) followed by generative AI-assisted learning effectiveness (r = .690, p < .001) and teacher responsibility (r = .679, p < .001). In the regression analysis, peer collaboration was found to be the strongest predictor of psychological well-being (β = .407) followed by generative AI-assisted learning effectiveness (β = .241) and teacher responsibility (β = .208). The model accounted for about 60.8% of the variance in psychological well-being. These results indicate that the successful implementation of GenAI needs to incorporate technology and healthy interaction with peers and teachers to achieve good experiences among students in higher education.