Elsa Natasya
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Digital Ethics and Learning Autonomy in Artificial Intelligence in Education: The Mediating Role of Trust in AI Nabilah Rahman; Elsa Natasya; Andi Dio Nurul Awalia; Muh. Yusril Anam; Della Fadhilatunisa
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): Desember 2025
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/s6eema73

Abstract

The rapid advancement of Artificial Intelligence in Education (AIED) has transformed digital learning practices while simultaneously raising critical concerns related to ethics, privacy, and user trust, which increasingly influence students’ ability to develop autonomous learning behaviors in AI-driven environments. This study aims to examine the relationships among Technology Readiness, Digital Learning Motivation, Digital Privacy Awareness, and Digital Ethics on Learning Autonomy, with Trust in AI serving as a mediating variable. A quantitative cross-sectional research design was employed involving 105 undergraduate students from Universitas Negeri Makassar, and data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that the proposed model explains 78.8% of the variance in Trust in AI and 84.3% of the variance in Learning Autonomy. Digital Learning Motivation shows a significant positive effect on Trust in AI and Learning Autonomy, while Digital Ethics also significantly influences both constructs; however, Technology Readiness and Digital Privacy Awareness do not significantly predict Trust in AI. Mediation analysis reveals that Trust in AI partially mediates the relationships between Digital Learning Motivation and Digital Ethics with Learning Autonomy. These findings demonstrate that psychological and ethical factors play a more decisive role than technical readiness in fostering trust and supporting autonomous learning in AIED contexts, highlighting the practical importance of integrating digital ethics education and motivational support into AI-based learning systems. Future research should employ longitudinal designs, broader samples, and additional variables such as AI literacy to further explore learning autonomy in AI-driven education.