AI Hallucinations in AI-Assisted Educational Decision-Making and Academic Honesty Intentions Among Undergraduates - Desitha Cahya; Putri Ramdani; Annajmi Rauf; Andi Baso Kaswar; M Miftach Fakhri (Academic Bright Collaboration, 2026)
Artificial Intelligence in Education (AIED) is increasingly used to improve learning efficiency, personalization, and academic productivity. however, persistent risks such as AI hallucinations, algorithmic bias, and limited transparency can undermine the reliability of AI outputs and create ethical vulnerabilities that threaten academic integrity. This study aims to examine how students’ perceptions of algorithmic bias, perceived transparency of AI systems, and digital literacy influence their intentions to behave honestly when using AI for academic purposes. A quantitative cross-sectional survey was administered to 97 undergraduate students with experience using generative AI tools, and the proposed relationships were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results indicate that algorithmic bias (β = 0.248; t = 2.420; p = 0.008), transparency (β = 0.188; t = 1.920; p < 0.001), and digital literacy (β = 0.499; t = 5.457; p = 0.027) each have positive and significant effects on honest behavior intentions, with digital literacy emerging as the strongest predictor. These findings imply that strengthening students’ digital literacy together with institutional efforts to promote transparent and fairness-aware AI use can reduce unethical practices and foster a more integrity-centered academic environment in AI-assisted learning, while also informing ethical behavior frameworks for AIED implementation in higher education.