AI-driven learning: evaluating the role of chatbots in enhancing student motivation and learning outcomes

Purpose The growing potential of artificial intelligence (AI) to enhance student learning engagement, particularly through chatbot-driven learning, has gained increasing attention in educational research. This study investigates the extent to which AI-powered chatbots can fulfill students’ basic psychological needs (namely, perceived autonomy, competence, and relatedness) and how fulfilling these needs impacts their motivation (including attitude and self-managed learning) and learning outcomes. Design/methodology/approach The data were collected from 312 students who experienced chatbot-driven learning and analyzed based on partial least squares structural equation modeling (PLS-SEM). Findings The findings revealed that perceived autonomy and competence exert significant positive effects on students’ attitudes and self-managed learning, while perceived relatedness influences only self-managed learning. Furthermore, both attitude and self-managed learning directly influence performance impact and experience satisfaction. In addition, attitude and self-managed learning serve as mediating mechanisms through which perceived autonomy and competence indirectly shape students’ perceptions of performance and satisfaction. Originality/value To date, there is limited investigation regarding the impact of chatbot-driven learning on student motivation and learning outcomes, especially in developing countries. Hence, this study can help researchers and practitioners understand how to foster student learning motivation and outcomes in AI-driven learning contexts.