Exploring the Usage Dynamics and Ethical Considerations of Generative AI Tools in Distance Learning Environments
Rita R.D.Luise
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Abstract:
This research investigates the complicated interplay between student engagement and academic integrity while using generative AI tools in distance learning environments. Utilizing a quantitative survey design with 100 participants, the study employed K-means cluster analysis, validated by the Elbow Method and Ward’s Hierarchical Method, to identify three distinct behavioural typologies: Supportive, Pragmatic and Over- Reliant. Findings from a One-Way ANOVA revealed statistically significant differences across all variables, identifying a critical “awareness gap”. While Supportive learners maintained high ethical standards, Over-Reliant cluster demonstrated a high frequency of AI dependency. Inferential analysis through Spearman’s Rho further confirmed that increased usage frequency strongly correlated with decreased ethical verification. The research also applies Bandura’s Theory of Moral Disengagement to explain how the isolation of distance learning environments facilitates displacement of responsibility onto AI tools. These results suggest that current institutional policies focusing solely on detection are insufficient. Instead, the study argues for a pedagogical shift toward process-based assessment and target ethical scaffolding to effectively transition from being “Over-Reliant” towards “Supportive” typology. These findings are crucial in understanding how digital autonomy in distance education influences the moral landscape of an AI-mediated classroom.
Keywords:
generative ai; distance learning; behavioural typologies; K-means clustering; Bandura; ANOVA; moral disengagement; Elbow Method; Wards hierarchical Method, Process-Based Assessment
Citation: Rita R.D.Luise (2026). Exploring the Usage Dynamics and Ethical Considerations of Generative AI Tools in Distance Learning Environments. Horizon J. Hum. Soc. Sci. Res. 8 (1), 78–91. https://doi.org/10.37534/bp.jhssr.2026.v8.n1.id1354.p78
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