Exploring the Impact of Generative AI Literacy on Students’ Academic Performance: An Ethical and Academic Fraud Perspective - Annas, Choiriyah; Nugraha, Jaka (CV. Muris Global Education, 2026)
This study aims to analyze the impact of generative AI literacy on student academic performance using an ethical and academic fraud perspective. This research is motivated by the increasing use of generative AI technology among students to complete academic assignments. Generative AI tools such as ChatGPT, Deepseek, Blackbox, and others have become popular tools among students, but their use raises ethical dilemmas and the potential for academic fraud that can impact academic integrity. This study used a quantitative approach with data collection through questionnaires distributed to students to identify the extent to which generative AI literacy impacts student academic performance, both positively and negatively. The data analysis technique used in this study was Structural Equation Modeling-Variance Based Structural Equation Modeling (VB-SEM) with a Generalized Structured Component Analysis (GSCA) approach. Furthermore, this study also examines the ethical implications of using this technology and its relationship to academic fraud such as plagiarism and academic dishonesty. The results are expected to provide insights for educational institutions in formulating policies regarding the use of Generative AI that balance technological innovation and academic integrity. This research can also serve as a reference for students in using technology responsibly to support quality academic achievement.