AI Chatbot Use in Higher Education: A Life-Course Perspective on Student Engagement and Cognitive Learning Outcomes - Muh. Nurfajri Syam; Muh Nurul Ainal Hakim; Della Fadhilatunisa; Saipul Abbas (Artificial Intelligence in Lifelong and Life-Course Education, 2026)
Purpose - The increasing use of artificial intelligence (AI) chatbots in higher education has reshaped how students engage with learning activities and develop cognitive skills. From a life-course education perspective, higher education represents a critical stage in early adulthood where learning experiences may influence long-term learning habits and readiness for lifelong learning. However, empirical studies integrating chatbot usage intensity, AI effectiveness, and student engagement within a single explanatory model remain limited, particularly in developing country contexts. This study examines the effects of AI chatbot usage intensity and perceived AI effectiveness on students’ cognitive learning outcomes, with student engagement positioned as a mediating mechanism.Design/methods/approach - A quantitative cross-sectional survey was conducted involving 88 undergraduate students who had experience using AI chatbots for academic purposes. Data were collected using a validated questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test direct and indirect relationships among the constructs.Findings - The results indicate that both chatbot usage intensity and AI effectiveness have significant positive effects on cognitive learning outcomes. These variables also significantly enhance student engagement, which in turn positively influences cognitive learning outcomes. Mediation analysis reveals that student engagement significantly mediates the relationship between AI effectiveness and cognitive learning outcomes, but not between chatbot usage intensity and cognitive learning outcomes, highlighting the dominant role of interaction quality over frequency of use.Research implications/limitations - The findings underscore the importance of designing AI-supported learning environments that prioritize pedagogical effectiveness and meaningful engagement rather than mere intensity of use. The cross-sectional design and reliance on self-reported data limit causal inference and generalizability.Originality/value - This study contributes to artificial intelligence in education research by integrating engagement as a mediating mechanism within a life-course framework, offering insights into how AI chatbot use during early adulthood may support sustainable cognitive development and lifelong learning readiness.