Responsible generative AI literacy (RAIL) and academic integrity in computer programming language classrooms: evidence from library and information management students

Abstract

The integration of Generative Artificial Intelligence (GenAI) tools into higher education, particularly in computer programming courses, has transformed how students engage with code. However, it also introduces ethical challenges, especially among Library and Information Management (LIM) students with limited technical backgrounds. This study explores the influence of Responsible Generative AI Literacy (RAIL) on students' adherence to academic integrity within programming education. Utilizing a quantitative cross-sectional approach, data were collected from 68 diploma-level LIM students enrolled in C++ and Python-based courses. Partial Least Squares Structural Equation Modeling (PLS-SEM) validated the measurement model, confirming high reliability, validity, and a significant positive relationship between RAIL and academic intergrity (β = 0.847, p < 0.001). The findings highlight RAIL's role in fostering ethical awareness, responsible AI tool usage, and resistance to academic dishonesty. The study recommends embedding AI ethics into curricula, mandating AI usage declarations, and adopting scenario-based learning to enhance students' ethical and technical fluency. By promoting RAIL, this research underscores the necessity of discipline-specific AI literacy frameworks to ensure both academic honesty and effective academic integrity integration in programming education.

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