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p-Index From 2021 - 2026
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All Journal Artificial Intelligence in Lifelong and Life-Course Education Artificial Intelligence in Educational Decision Sciences
Andi Akram Nur Risal
Universitas Gadjah Mada
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Learning Autonomy and Effectiveness in AI-Supported Engineering Education Integrating Technology Acceptance and Motivation Haeril Anwar; Ismawati; Nurrahmah Agusnaya; Andi Akram Nur Risal; Dary Mochammad Rifqie
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.v1i2.14

Abstract

Purpose – This study examines the influence of learning autonomy on learning effectiveness in artificial intelligence supported learning among engineering students by extending the Technology Acceptance Model with motivational and psychological factors.Design/methods/approach – A quantitative cross-sectional survey was conducted involving 90 engineering students from a public university in Indonesia who had experience using artificial intelligence tools for academic learning. Data were analyzed using partial least squares structural equation modeling to examine the relationships among perceived usefulness, self-efficacy, willingness for autonomous learning, and learning effectiveness and autonomy.Findings – The results indicate that perceived usefulness, self-efficacy, and willingness for autonomous learning all have significant positive effects on learning effectiveness and autonomy. Willingness for autonomous learning emerged as the strongest predictor, highlighting the central role of students’ internal motivation and readiness to manage their own learning processes in AI-supported environments.Research implications/limitations – The study is limited by its cross-sectional design, reliance on self-reported data, and a sample restricted to engineering students from a single institution, which may limit generalizability.Originality/value – This study extends the Technology Acceptance Model by integrating learning autonomy and motivational factors within an artificial intelligence supported learning context, offering empirical evidence to inform the design of balanced and student-centered AI-enhanced learning in higher education.
Data-Driven Clustering of Stunting Prevention Services for Pregnant Women and Infants Using Fuzzy C-Means Hanum Zalsabilah Idham; Ayu Safitri; Andi Akram Nur Risal; Dewi Fatmarani Surianto; Firdaus
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.v1i2.22

Abstract

Purpose – This study addresses persistently high stunting rates in South Sulawesi, Indonesia, which remain above national targets despite declining trends. We developed a clustering model to overcome limitations of traditional methods in handling complex health data with overlapping characteristics, aiming to identify priority regions requiring targeted interventions.Methods – Using 2,267 structured records from Satu Data Indonesia covering maternal and child health indicators, we implemented Fuzzy C-Means (FCM) algorithm with systematic preprocessing, optimal cluster determination via Elbow Method, and quality validation using Silhouette Coefficient.Findings – Analysis revealed three distinct clusters for pregnant women (representing good, moderate, and low service coverage areas) and three corresponding clusters for infants. Validation showed Silhouette values ranging from 0.204 to 0.645, indicating variable cluster separation quality with Cluster 0 pregnant women achieving highest cohesion (0.638) and Cluster 2 infants showing strongest separation (0.645).Research limitations – Data quality limitations affected cluster cohesion in some areas, particularly Cluster 1 infants (0.204 Silhouette value), constraining generalizability. The FCM approach accommodates real-world data complexity better than rigid clustering methods but requires high-quality input data.Originality – This research contributes an adaptive framework for evidence-based stunting prevention through sophisticated data-driven segmentation. Findings offer immediate practical value for health policymakers in resource allocation and intervention planning, with potential adaptation to other regional contexts facing similar public health challenges.
Co-Authors Ayu Safitri Dary Mochammad Rifqie Dewi Fatmarani Surianto Firdaus Haeril Anwar Hanum Zalsabilah Idham Ismawati Nurrahmah Agusnaya