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

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Benefits, Convenience, Ethics, and Anxiety Shaping Indonesian Students’ Intentions to Adopt Generative Artificial Intelligence Intan Ramadhani Hasbullah; Andi Imam Ardiansyah; Elma Nurjannah; Stephen Amukune
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.v1i2.15

Abstract

Purpose – This study examines Indonesian university students’ behavioral intention to adopt generative artificial intelligence by extending the technology acceptance model with ethical concern and artificial intelligence anxiety. It evaluates how perceived usefulness, perceived ease of use, ethical concern, and artificial intelligence anxiety jointly shape adoption intention in higher education.Design/methods/approach – A quantitative cross-sectional survey was administered to 96 active undergraduate students at a public university in Indonesia. The extended model was analyzed using partial least squares structural equation modeling to estimate the predictive power and the significance of structural relationships among constructs.Findings – The structural model explained 64.5% of the variance in behavioral intention. Perceived usefulness was the strongest predictor, followed by ethical concern and perceived ease of use. Artificial intelligence anxiety did not significantly influence behavioral intention, suggesting that functional value and ethical awareness outweighed affective apprehension among experienced users.Research implications/limitations - Institutions should prioritize practical integration and clear ethical guidance for generative artificial intelligence use rather than focusing primarily on reducing anxiety. Generalizability is limited by the cross-sectional design, small sample size, and a sample dominated by science and technology disciplines.Originality/value - This study provides empirical evidence that ethical concern functions as a regulatory facilitator rather than a barrier in generative artificial intelligence acceptance, offering a refined lens for responsible adoption policies in Indonesian 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 Andi Imam Ardiansyah Annajmi Rauf Fredi Ganda Putra Intan Ramadhani Hasbullah Saipul Abbas Stephen Amukune