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All Journal Artificial Intelligence in Lifelong and Life-Course Education Artificial Intelligence in Educational Decision Sciences
Stephen Amukune
MATE Institute of Education
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.
Predicting Student Dependency on ChatGPT for Academic Tasks Using Naive Bayes Classification Risha Febrianti; Sul Fitriana; Asrafah; Stephen Amukune
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (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.v1i2.23

Abstract

Purpose – This study aims to predict and classify the level of student dependency on ChatGPT in completing academic tasks using the Naive Bayes algorithm to support data-driven decision making in higher education.Methods – A quantitative survey approach was employed involving 254 active undergraduate students from the Department of Informatics and Computer Engineering at a public university in Indonesia. Data were collected through a Likert-scale questionnaire measuring five behavioral indicators: purpose of ChatGPT use, interaction frequency and duration, understanding of generated outputs, trust in AI responses, and learning independence. The collected data were cleaned, numerically encoded, and labeled into three dependency categories (low, medium, high). A Naive Bayes classification model was implemented using Orange Data Mining and evaluated under three data split scenarios: 90:10, 80:20, and 70:30.Findings – The results indicate that the 70:30 data split achieved the highest classification performance, with an AUC value of 0.973, accuracy of 85.3%, F1-score of 0.866, and precision of 0.909. These results demonstrate that the Naive Bayes algorithm is effective in identifying distinct patterns of student dependency on ChatGPT based on multidimensional behavioral data.Research limitations – This study is limited to a single academic program and relies on self-reported questionnaire data, which may constrain the generalizability of the findings across different educational contexts.Originality – This study provides empirical evidence on the application of probabilistic classification models to assess student dependency on generative AI, contributing to educational decision sciences by informing institutional policies on balanced and responsible AI use in higher education.
Co-Authors Andi Imam Ardiansyah Asrafah Elma Nurjannah Intan Ramadhani Hasbullah Risha Febrianti Sul Fitriana