Factors Associated with the Ethical Use of Generative AI in Academic Content Development while Maintaining Academic Integrity: Evidence from Distance Learning

Abstract

While the use of generative artificial intelligence (GenAI) in academic work offers benefits such as increased efficiency and improved content development, it has also raised concerns regarding academic integrity, source and content verification, and the ethical use of AI in academic contexts. Empirical evidence examining these factors collectively within distance-learning contexts remains limited. This study examined factors associated with the ethical use of GenAI in academic content development while maintaining academic integrity among distance-learning content developers. A quantitative approach was employed at Allama Iqbal Open University (AIOU), Pakistan. Data were collected from 121 distance-learning content developers using a validated questionnaire. Descriptive statistics, Pearson correlation, and multiple linear regression were used to examine the levels, relationships, and predictors of ethical practice. Participants reported high levels of perceptions toward AI and academic integrity (M = 4.01), knowledge (M = 3.91), and ethical practice (M = 3.69), while AI capabilities and skills were at a moderate level (M = 3.30). Pearson correlations showed significant positive relationships among all study variables. Multiple regression indicated that knowledge (β = .380, p = .003) and AI capabilities and skills (β = .324, p = .001) significantly predicted ethical practice, whereas perception did not make a significant independent contribution (β = .111, p = .257). The model explained 50.2% of the variance in ethical practice (R² = .502). The findings indicate that ethical GenAI use in distance-learning content development is more strongly associated with knowledge of academic integrity and practical AI capabilities and skills than with positive perceptions alone. The study highlights the need for intentional professional development in AI literacy and academic integrity, supported by human oversight, verification, continuous learning, and clear institutional guidance.