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All Journal Artificial Intelligence in Lifelong and Life-Course Education Artificial Intelligence in Educational Decision Sciences
M. Miftach Fakhri
Universitas Pendidikan Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Digital Balance in the AI Era: A Life-Course Perspective on AI Interaction, Digital Well-Being, and Academic Performance among Engineering Students Fauziyah Alfathyah; Nur Aisyah Fadliyah Faizal; Andi Dio Nurul Awalia; Andi Baso Kaswar; M. Miftach Fakhri
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.v1i1.1

Abstract

Purpose – The increasing integration of artificial intelligence (AI) in higher education offers substantial benefits for learning efficiency and personalization, yet it also raises concerns regarding digital ethics, learner autonomy, and digital well-being. From a life-course education perspective, early adulthood represents a critical transitional stage in which patterns of AI interaction may shape long-term learning habits and readiness for lifelong learning. However, empirical evidence examining how multidimensional AI interactions influence academic outcomes through psychological mechanisms remains limited, particularly in developing country contexts. This study investigates the effects of cognitive, affective, and social-ethical interactions with AI on academic performance among Indonesian engineering students, with digital well-being positioned as a mediating mechanism.Design/methods/approach – A quantitative cross-sectional survey was conducted with 103 engineering students from multiple universities, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).Findings – The findings indicate that cognitive interaction with AI significantly enhances academic performance, while affective interaction primarily contributes to digital well-being. Notably, higher levels of digital well-being are associated with reduced academic performance, suggesting a paradox in which increased comfort and convenience from AI may weaken sustained cognitive engagement. Digital well-being significantly mediates the relationship between affective interaction and academic performance, revealing potential risks of emotional overreliance on AI.Research implications/limitations – These results highlight the importance of balanced and self-regulated AI use in higher education and underscore the need to design AI-supported learning environments that foster cognitive engagement while sustaining digital well-being. From a life-course perspective, the findings suggest that AI interaction patterns formed during early adulthood may have implications for lifelong learning autonomy and educational sustainability.Originality/value – This study provides empirical evidence on multidimensional AI interaction in higher education from a life-course perspective and emphasizes the importance of ethical and responsible AI integration to safeguard academic performance and student well-being.
AI Awareness, Literacy, and Social Influence Predict Ethical Reasoning and Responsible Use in Higher Education Nurul Febrianti; Aristia Anastasya Diandra; Andi Dio Nurul Awalia; Della Fadhilatunnisa; M. Miftach Fakhri
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.v1i2.17

Abstract

Purpose – This study investigates how AI awareness, AI literacy, and social influence shape students’ AI ethics and, consequently, responsible AI use in higher education.Design/methods/approach – A quantitative cross-sectional survey was conducted with 101 university students in South Sulawesi, Indonesia, who had experience using AI-based learning tools. Data were analyzed using partial least squares structural equation modeling to assess measurement validity and test structural relationships, including the mediating role of AI ethics.Findings – AI awareness and AI literacy have significant positive effects on AI ethics, with AI literacy emerging as the strongest predictor. Social influence shows a significant negative association with AI ethics, indicating that unregulated peer and environmental pressure may encourage AI adoption while weakening ethical sensitivity. AI ethics significantly predicts responsible AI use and mediates the effects of AI awareness, AI literacy, and social influence on responsible use. These results highlight that responsible AI engagement depends not only on cognitive readiness but also on the ethical norms governing how AI is used in academic contexts.Research implications/limitations – The study is limited by its cross-sectional design, self-reported data, and a sample restricted to one region, which may limit causal inference and generalizability.Originality/value – This study provides empirical evidence that AI ethics is a central mechanism linking cognitive and social factors to responsible AI use, informing institutional AI governance, literacy programs, and ethical policy development in higher education.
AI-Driven Clustering of Social Media Consumption Patterns and Daily Productivity Using K-Means and DBSCAN in Multigenerational Respondents Nurrahmah Agusnaya; Putri Nirmala; M. Miftach Fakhri; Fadhil Zil Ikram
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.v1i1.8

Abstract

Purpose – The rapid development of digital technology has made social media an integral part of life across generations, yet its intensive use raises growing concerns regarding its impact on daily productivity. This study aims to analyze patterns of social media consumption behavior and their relationship with productivity across age groups using a dual clustering approach based on the K-Means and DBSCAN algorithms.Methods – The study utilizes secondary data from 3,000 multigenerational respondents, processed using Orange Data Mining through stages of data selection, normalization, and unsupervised clustering. K-Means is employed to segment respondents based on proximity to cluster centroids, while DBSCAN is applied to identify density-based behavioral patterns and detect outliers representing extreme digital usage behaviors.Findings – The results indicate that K-Means effectively maps macro-level clusters primarily differentiated by age, achieving an average Silhouette score of 0.537, which reflects stable and well-separated segmentation. In contrast, DBSCAN demonstrates superior capability in identifying micro-level behavioral patterns, particularly respondents exhibiting extreme characteristics such as excessive screen time and non-productive application usage, despite yielding a lower overall Silhouette value. The comparative analysis highlights that K-Means is more suitable for demographic-based segmentation, whereas DBSCAN provides deeper insights into localized and atypical digital behavior.Research limitations – The analysis is based on a randomly sampled subset of a publicly available dataset, which may limit the generalisability of the findings across different cultural, occupational, and socioeconomic contexts. Future studies are encouraged to incorporate longitudinal data and additional behavioral variables to capture temporal dynamics and causal relationships between social media usage and productivity.Originality – This study contributes by systematically comparing centroid-based and density-based clustering approaches within a multigenerational framework to reveal both macro-demographic and micro-behavioral patterns of digital consumption. The proposed dual clustering strategy offers a novel analytical perspective for designing more adaptive and evidence-based digital literacy and productivity enhancement policies.
Co-Authors Andi Baso Kaswar Andi Dio Nurul Awalia Andi Dio Nurul Awalia Aristia Anastasya Diandra Della Fadhilatunnisa Fadhil Zil Ikram Fauziyah Alfathyah Nur Aisyah Fadliyah Faizal Nurrahmah Agusnaya Nurul Febrianti Putri Nirmala