Comparing Human and Generative AI Ethical Decision-Making in Computer Ethics Education

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This study investigates how humans and generative artificial intelligence systems approach ethical dilemmas in information technology. Empirical research comparing human ethical reasoning with both untrained and ethically trained generative AI remains limited. Grounded in information ethics, the study examines reasoning processes underlying moral judgments and explores AI-supported ethical awareness in education. Conducted in an undergraduate Information Ethics and Security course, an exploratory mixed methods design was employed. Forty-eight real-life-inspired ethical scenarios were evaluated by thirty-three preservice teachers, a generative AI model, and an AI model trained with an ethical decision-making framework. Quantitative analyses and non-parametric tests examined similarities and differences between human and AI judgments. Semi-structured interviews with eight participants and a generative AI application provided deeper insights into moral reasoning patterns. Findings suggest that AI demonstrates rule-based precision but lacks contextual and emotional depth, highlighting its potential to meaningfully support ethical decision-making and inform ethically sensitive AI-supported educational applications.