Article Per Year (5 Year)
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
Akhmad Affandi
Dresden University
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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AI Dependency and Critical Thinking in Higher Education: A Life-Course Perspective on Ethical Awareness and Algorithmic Bias Jabal Nur Popalia; Muh. Al-Habsy; Muh. Akbar; Akhmad Affandi
Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 1 (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.v1i1.3

Abstract

Purpose – The rapid adoption of artificial intelligence (AI) in higher education has transformed how students engage with learning tasks, raising concerns about dependency, ethical awareness, and algorithmic bias. From a life-course education perspective, early adulthood represents a critical developmental stage in which patterns of AI use may shape long-term critical thinking and lifelong learning dispositions. However, empirical studies integrating AI dependency, ethical awareness, and algorithmic bias awareness in relation to students’ critical thinking remain limited. This study examines the effects of AI dependency, ethical awareness, and algorithmic bias awareness on university students’ critical thinking skills in the context of Indonesian higher education.Design/methods/approach – A quantitative cross-sectional design was employed. Data were collected from 110 undergraduate students across four universities in South Sulawesi, Indonesia, using purposive sampling. A validated questionnaire measured AI dependency, ethical awareness, algorithmic bias awareness, and critical thinking skills. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS. Findings – The results indicate that all three variables significantly and positively influence students’ critical thinking skills. Algorithmic bias awareness exhibits the strongest effect, followed by AI dependency and ethical awareness. These findings suggest that critical awareness of AI limitations contributes more substantially to critical thinking development than the intensity of AI use alone.Research implications/limitations – The cross-sectional design limits causal interpretation, and the dominance of early-semester STEM students constrains generalizability. Potential moderating factors were not examined. Originality/value – This study contributes to the literature on artificial intelligence in education by integrating ethical awareness and algorithmic bias awareness within a life-course framework, highlighting the central role of critical AI literacy in supporting sustainable critical thinking development in higher education.
Data-Driven Obesity Classification Integrating Genetic and Lifestyle Determinants Using Naive Bayes Yusion Gandjang; Amaliah Safitri K; Nabila Dwi Anugra; Iyang Yuyung S; Akhmad Affandi
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.v1i2.21

Abstract

Purpose – This study aims to develop a data-driven obesity classification framework that integrates genetic predisposition and lifestyle determinants using the Naive Bayes algorithm, while empirically evaluating optimal training–testing data proportions for health decision support systems.Methods – A systematic computational workflow was applied to a public obesity dataset comprising 2,112 records, which was refined to 1,259 valid instances after preprocessing. Genetic indicators and lifestyle-related variables were encoded and classified into four obesity categories: normal weight, obesity type I, obesity type II, and obesity type III. The Naive Bayes model was evaluated using three training–testing data partition ratios (75:25, 80:20, and 85:15). Model performance was assessed using six metrics: Area Under the Curve (AUC), classification accuracy, F1-score, precision, recall, and Matthews Correlation Coefficient.Findings – The results demonstrate that the 80:20 and 85:15 data partitions achieved the highest performance, with an accuracy of 0.878 and an AUC of 0.979. The model showed excellent sensitivity in identifying severe obesity cases, while moderate misclassification occurred between obesity type I and type II due to phenotypic overlap in lifestyle patterns.Research limitations – This study relies on a single public dataset and lacks population-specific genetic calibration, which may limit generalizability to diverse regional contexts.Originality – This study provides empirical validation of a probabilistic obesity classification framework that integrates genetic and lifestyle factors, offering an interpretable and computationally efficient approach to support data-driven health decision making.
Co-Authors Amaliah Safitri K Iyang Yuyung S Jabal Nur Popalia Muh. Akbar Muh. Al-Habsy Nabila Dwi Anugra Yusion Gandjang