Muh. Al-Habsy
Universitas Negeri Makassar

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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.