AI Hallucinations in AIED and Their Impact on Students' Intentions to Behave Honestly: A PLS-SEM Analysis of JTIK UNM Students

Desitha Cahya; Putri Ramdani; Annajmi Rauf; Andi Baso Kaswar; M Miftach Fakhri

Authors

Desitha Cahya Universitas Negeri Makassar Author
Putri Ramdani Universitas Negeri Makassar Author
Annajmi Rauf Universitas Negeri Makassar Author
Andi Baso Kaswar Okayama University Author
M Miftach Fakhri Universitas Pendidikan Indonesia Author

Keywords:

Algorithmic Bias, Artificial Intelligence in Education, Digital Literacy, Honest Behaviour, Transparency

Abstract

Artificial Intelligence in Education (AIED) is increasingly used to support learning efficiency, personalization, and academic productivity. However, issues such as AI hallucination, algorithmic bias, limited system Transparency, and variations in students’ Digital Literacy present ethical risks that may undermine academic integrity. These challenges indicate a gap between the ideal function of AI as a learning assistant and its practical use, which remains prone to plagiarism and misuse. This study aims to analyze how students’ perceptions of algorithmic bias, Transparency in AI systems, and Digital Literacy influence their Honest Behavior when using AI for academic purposes. A quantitative research method was employed using a survey design, and data were analyzed through Partial Least Squares Structural Equation Modeling to empirically examine the relationships among variables. The results show that algorithmic bias, Transparency, and Digital Literacy each have a positive effect on honest behavior, with Digital Literacy emerging as the strongest predictor. These findings suggest that students with better digital skills and awareness of AI mechanisms are more capable of using AI responsibly and ethically. This study concludes that higher education institutions need to strengthen policies related to ethical AI use and enhance students’ Digital Literacy to foster an academically honest environment. The study contributes to the development of ethical behavior frameworks in the AIED context and provides considerations for institutions to improve integrity in AI-assisted learning.

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