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All Journal Artificial Intelligence in Lifelong and Life-Course Education Artificial Intelligence in Educational Decision Sciences
Annajmi Rauf
Universitas Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Artificial Intelligence Interaction in Higher Education: A Life-Course Perspective on Digital Well-Being, Learning Outcomes, Motivation, and Ethical Awareness Ikrananda; Indah Amaliah; Annajmi Rauf; Muh. Yusril Anam; Irwansyah Suwahyu
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.v1i1.2

Abstract

Purpose – The increasing integration of artificial intelligence (AI) in higher education offers significant opportunities to enhance learning effectiveness, yet it also raises concerns related to digital well-being, learner motivation, and ethical awareness. From a life-course education perspective, early adulthood represents a critical transitional phase in which patterns of interaction with AI may shape long-term learning habits and readiness for lifelong learning. However, empirical evidence examining how AI interaction influences learning outcomes through psychological and instructional mechanisms remains limited. This study examines the effects of student interaction with AI on learning outcomes, learning motivation, and ethical awareness, with digital well-being and instructional design quality positioned as mediating variables.Design/methods/approach – A quantitative cross-sectional survey was conducted with 145 undergraduate students at a public university in Indonesia. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine direct and mediating relationships among the proposed constructs.Findings – The results indicate that student interaction with AI has a significant positive effect on digital well-being, instructional design quality, learning motivation, and learning outcomes. Digital well-being and instructional design quality serve as important mediating mechanisms through which AI interaction enhances motivation and academic achievement. However, interaction with AI does not directly improve students’ ethical awareness, suggesting that ethical sensitivity does not emerge automatically through AI use without explicit pedagogical intervention.Research implications/limitations – These findings underscore the importance of designing AI-supported learning environments that promote cognitive engagement, digital well-being, and pedagogical quality while deliberately integrating ethical instruction. The study is limited by its cross-sectional design, single-institution context, and reliance on self-reported data.Originality/value – This study contributes to the literature on artificial intelligence in education by integrating digital well-being and instructional design quality as mediating mechanisms within a life-course framework, offering insights into how AI interaction during early adulthood may influence sustainable and responsible lifelong learning.
Academic Dependency, AI Literacy, and Cognitive Offloading Predict Students’ Cognitive Ability in Generative AI Learning Andini Noviyanti Fitriani; Rezky Risaldy; Annajmi Rauf; Shera Afidatunisa
Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 2 (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.v1i2.18

Abstract

Purpose – This study examines the cognitive effects of generative artificial intelligence use in higher education by testing whether academic dependency, AI literacy, and cognitive offloading predict students’ cognitive ability.Design/methods/approach – A quantitative cross-sectional survey was conducted with 93 undergraduate students at Universitas Negeri Makassar who actively use generative AI tools for academic purposes. Data were collected through a structured online questionnaire and analyzed using partial least squares structural equation modeling to evaluate measurement reliability and validity and to test structural relationships among academic dependency, AI literacy, cognitive offloading, and student cognitive ability.Findings – The structural model shows that academic dependency, AI literacy, and cognitive offloading positively and significantly predict student cognitive ability. AI literacy is the strongest predictor, indicating that students’ capacity to understand, evaluate, and use AI outputs critically is central to cognitive development. The findings also suggest that adaptive dependency can function as productive scaffolding, while strategic cognitive offloading may support higher-order thinking by reallocating limited cognitive resources.Research implications/limitations – The cross-sectional design limits causal inference, self-reported measures may introduce bias, and a single-institution context limits generalizability.Originality/value – This study provides integrated empirical evidence on the cognitive impact of generative AI use by jointly modeling academic dependency, AI literacy, and cognitive offloading, informing balanced AI literacy interventions and responsible AI governance in higher education.
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.
Co-Authors Andini Noviyanti Fitriani Elma Nurjannah Fredi Ganda Putra Ikrananda Indah Amaliah Irwansyah Suwahyu Muh. Yusril Anam Rezky Risaldy Saipul Abbas Shera Afidatunisa