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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 Vol 1 No 1 (2026): Artificial Intelligence in Lifelong and Life-Course Education
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aillce.v1i1.4

Abstract

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.
AI-Based Educational Decision Analytics: K-Means Clustering of University Students’ Digital Learning Readiness Using Limited and Full Attitude Schemes Annajmi Rauf; Elma Nurjannah; Fredy Ganda Putra; Saipul Abbas
Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i1.19

Abstract

Purpose – Advancements in digital learning require students to be adequately prepared both psychologically and technologically. However, students’ attitudes toward digital learning have not yet been systematically mapped using data-driven segmentation approaches. This study aims to classify university students based on similarities in their attitudes toward digital learning using the K-Means clustering algorithm and to identify the most influential dimensions distinguishing levels of digital readiness.Methods – This study employed an exploratory quantitative design using survey data collected from 469 university students. Clustering was conducted using the K-Means algorithm implemented in the Orange Data Mining application. Two variable schemes were compared: a limited scheme comprising four constructs (Psychological Traits, Growth Mindset, Learner Motivation & Engagement, and Digital Competence) and a full scheme including six constructs with the addition of Digital Readiness & Mindfulness and Student Satisfaction. Data were normalized using Min–Max normalization, and cluster quality was evaluated using the Silhouette Coefficient.Findings – Results indicate that both schemes consistently produced two optimal clusters representing students with high and low levels of digital learning readiness. The highest Silhouette Coefficient values were obtained at K = 2 for both schemes (0.335 for the limited scheme and 0.323 for the full scheme). Psychological Traits and Learner Motivation & Engagement emerged as the most significant differentiating dimensions between clusters, followed by Digital Competence.Research limitations – The findings are limited to self-reported data and a single institutional context, which may constrain generalizability. Additionally, the cross-sectional design does not capture changes in student attitudes over time.Originality – This study contributes a comparative clustering framework that integrates psychological, motivational, and technological dimensions to map digital learning readiness. The results provide a practical foundation for designing adaptive and personalized digital learning strategies based on student readiness profiles.