BETWEEN TRUST AND UNCERTAINTY: HOW STUDENTS CONSTRUCT ETHICAL BOUNDARIES IN AI-DRIVEN LEARNING
DOI:
https://doi.org/10.18623/rvd.v22.n4.3741Keywords:
AI in Education, Academic Trust, Student Ethics, Authorship, Emotional BurdenAbstract
As artificial intelligence becomes more embedded in academic settings, questions of trust, authorship, and ethical responsibility become increasingly urgent—especially in contexts where institutional policy is vague or absent. This qualitative study explores how undergraduate students interpret and navigate the ethical use of AI tools in learning environments lacking clear guidelines. Twelve students participated in semi-structured interviews focused on their perceptions of fairness, authorship, and moral boundaries when engaging with AI technologies. Thematic analysis revealed three key patterns shaping students’ trust and use of AI: reading institutional signals to interpret what is implicitly allowed, managing emotional risks such as guilt or anxiety in the absence of policy clarity, and maintaining personal authority over their academic work despite AI involvement. Rather than relying solely on rules, students constructed their own frameworks for responsible use—often guided by emotional cues, peer discussion, and personal values. These findings suggest that ethical AI literacy requires more than technical competence; it demands shared dialogue, emotional safety, and participatory policy-making. The study calls for institutions to move beyond compliance models and engage students as co-authors of ethical practice in AI-augmented education.
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