Breaking the “Non-Exact Sciences” Myth: Disciplinary Sensitivity and AI Ethical Awareness Among Pre-Service Language Teachers - Al Ashadi Alimin; Hastiani Hastiani; Augusto Da Costa; Simão Lopes Cardoso (IJECA (International Journal of Education and Curriculum Application), 2026)
The rapid integration of Generative Artificial Intelligence (GenAI) in higher education has sparked global concerns regarding academic integrity and regulation. However, existing literature often generalizes student perceptions through a binary STEM versus non-STEM lens, obscuring crucial intradisciplinary variations. This study investigated the disparities in ethical awareness and regulatory demands regarding GenAI among university students, specifically challenging the monolithic categorization of Non-Exact Sciences. Employing a quantitative cross-sectional survey, data from 721 Indonesian undergraduates were collected using a 25-item Likert-scale questionnaire, validated via Pearson correlation with high reliability (Cronbach’s α = 0.957). Statistical analysis using the Kruskal-Wallis H test revealed a significant divergence from traditional assumptions. While macro-analysis confirmed Exact Science students generally exhibit higher ethical awareness, granular post-hoc analysis uncovered Language Education students represent a positive anomaly. These findings empirically operationalize the concept of 'epistemological proximity,' the degree to which GenAI intersects with a discipline's core ontology, such as text production in language studies. As a measurable predictor of AI ethical awareness, students in text-centric disciplines (Language Education) demonstrated significantly higher ethical (Mean Rank = 419.64) and regulatory demand scores (Mean Rank = 403.17) compared to other Non-Exact Sciences (p < .001), rendering their profiles statistically indistinguishable from Exact Sciences. Thus, epistemological proximity is a stronger and more operational predictor of ethical awareness than broad binary disciplinary labels. The study concludes that the non-exact sciences  category is no longer relevant for AI adoption studies and recommending policymakers develop discipline-sensitive AI literacy programs addressing the specific regulatory vacuums.