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All Journal Artificial Intelligence in Educational Decision Sciences Journal of Applied Artificial Intelligence in Education Journal of Smart Education and Emerging Technology
Saif Mohammed
Bilkent University
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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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.
Effects of Artificial Intelligence Integration on Design Mindset, Creativity, and Reflection Khaerul Amri; Intan Novita Kowaas; Andro Ruben Runtu; Saif Mohammed; Rifky Muhajji
Journal of Applied Artificial Intelligence in Education Vol 1, No 1 (2025): July 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v1i1.4

Abstract

Artificial intelligence (AI) is increasingly embedded in design-based learning because it can accelerate ideation, support rapid iteration, and enable human–AI collaboration; however, a persistent challenge is maintaining an appropriate balance between AI-driven automation and human agency while ensuring that students’ design mindset, creativity, and reflective thinking are genuinely strengthened. This study aimed to examine the perceived effects of AI integration on students’ design mindset, creativity, and critical reflection in higher education. A quantitative cross-sectional design was employed with purposive sampling of 96 university students (predominantly female; mean age ≈20 years) who had used AI tools in learning and design activities; data were collected via an online Likert-scale questionnaire distributed from October to November 2024 and analyzed using descriptive statistics (means and sums). The results indicate that students reported generally moderate-to-positive perceptions of AI’s contribution across all constructs, with overall mean scores suggesting beneficial support for design mindset (M≈2.59) and creativity (M≈2.59), and relatively stronger support for reflection (M≈2.68), particularly in helping students understand their learning/creative processes and learn from mistakes. These findings imply that higher education institutions such as Makassar State University should integrate AI more strategically as a co-creative learning partner, complemented by structured training for both instructors and students to maximize creative and reflective gains while safeguarding human control; overall, AI shows strong potential to enhance design-oriented learning, but deeper implementation and longitudinal evaluation are recommended.
Cyberbullying Patterns Psychological Impacts and Coping Strategies on Social Media among Adolescents Andi Siti Aulia Akbar; Muh Fuad; Saif Mohammed
Journal of Smart Education and Emerging Technology Vol 1 No 1 (2025) : July
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/jseet.v1i1.158

Abstract

Background/Context: The rapid expansion of social media has transformed patterns of interaction among adolescents, creating opportunities for communication but also posing serious challenges such as cyberbullying. This phenomenon has become a significant concern due to its potential to harm victims’ self-confidence, social relationships, academic performance, and mental health.Objective/Purpose: This study aims to analyze patterns, impacts, preventive efforts, and personal attitudes toward cyberbullying on social media, as perceived by adolescents.Method: A quantitative descriptive approach with a cross-sectional design was employed. Data were collected from 71 respondents through a Likert-scale questionnaire covering four aspects: cyberbullying patterns, impacts, preventive measures, and personal attitudes.Results: The findings indicate that respondents strongly acknowledged the presence of cyberbullying patterns on social media, recognized its negative impacts on victims, emphasized the importance of preventive measures, and expressed firm personal attitudes against such behavior. The responses consistently reflected a shared recognition that cyberbullying is a serious issue in digital interactions.Conclusion: The study concludes that cyberbullying is a pervasive problem in adolescent social media use, requiring structured prevention strategies, legal reinforcement, digital literacy education, and collective participation. Collaborative efforts among individuals, communities, educators, and technology platforms are crucial to create safer and healthier online environments for young people.
Co-Authors Andi Siti Aulia Akbar Andro Ruben Runtu Intan Novita Kowaas Khaerul Amri Muh Fuad Rifky Muhajji Wahyuni Edsa Safira