Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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This Author published in this journals
All Journal Artificial Intelligence in Lifelong and Life-Course Education Artificial Intelligence in Educational Decision Sciences
Saif Mohammed
Bilkent University
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Ethical Awareness, Perceived Usefulness, and AI Literacy Predict University Students’ Intentions to Use AI Tools Muhammad Ghazi Saputra; Elsa Wulandari Tambunan; Andi Nurhalisa Dwiani; Devi Miftahul Jannah; Saif Mohammed
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.v1i2.16

Abstract

Purpose – This study examines how ethical awareness and perceived usefulness shape university students’ intentions to use artificial intelligence tools, and whether artificial intelligence literacy mediates these relationships in higher education.Design/methods/approach – A quantitative cross-sectional survey was administered to 85 diploma and undergraduate students with prior experience using artificial intelligence for academic activities. The research model included perceived usefulness, ethical awareness, artificial intelligence literacy, and behavioral intention to use. Data were analyzed using partial least squares structural equation modeling with 5,000 bootstrapping resamples to evaluate measurement quality, test direct effects, and assess mediation.Findings – Perceived usefulness significantly predicts behavioral intention to use artificial intelligence tools and also strengthens artificial intelligence literacy. Ethical awareness significantly increases artificial intelligence literacy but does not directly predict behavioral intention. Artificial intelligence literacy significantly predicts behavioral intention and mediates the effects of both perceived usefulness and ethical awareness on intention. These findings suggest that ethical awareness alone may increase caution unless supported by sufficient literacy that enables students to evaluate benefits, limitations, and risks of artificial intelligence tools.Research implications/limitations – The cross-sectional design, purposive sampling, and a single-institution sample limit causal inference and generalizability. Future studies should use larger and more diverse samples and longitudinal designs.Originality/value – This study provides empirical evidence that artificial intelligence literacy functions as a key mediating mechanism linking ethical awareness and perceived usefulness to artificial intelligence usage intention, informing responsible adoption strategies in higher education.
Supporting Academic Library Collection Decisions Using K-Means–Based Book Recommendation Wahyuni Edsa Safira; Saif Mohammed
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.v1i2.25

Abstract

Purpose - This study aims to develop a data-driven book recommendation system to support academic library collection management using the K-Means clustering method.Methods - The study utilized book borrowing data from the Library of the Department of Informatics and Computer Engineering at Makassar State University collected over a 22-month period. Borrowing records were grouped by book categories and monthly borrowing frequencies, then processed into numerical variables. The K-Means algorithm was applied to identify borrowing pattern clusters, and cluster quality was evaluated using the Silhouette Coefficient to assess cohesion and separation.Findings - The analysis produced three distinct clusters representing different borrowing behaviors. Programming and information technology books formed the most frequently borrowed cluster, research methodology books showed increased demand during specific academic periods, and education and learning methods books exhibited relatively lower borrowing intensity. The average Silhouette Coefficient value of 0.35 indicates a moderate yet acceptable clustering structure for recommendation and managerial purposes.Research limitations - This study is limited to historical transaction data from a single departmental library and does not incorporate user profiles or qualitative preference data, which may restrict generalizability to other academic library contexts.Originality - This study contributes empirical evidence on the use of K-Means clustering for book recommendation and decision support in academic libraries, demonstrating how borrowing pattern analysis can inform data-driven collection management and improve the relevance of library services. The findings also highlight the practical role of clustering analytics in supporting efficient resource allocation and evidence-based planning within higher education libraries and departmental level strategic decisions.
Co-Authors Andi Nurhalisa Dwiani Devi Miftahul Jannah Elsa Wulandari Tambunan Muhammad Ghazi Saputra Wahyuni Edsa Safira