Mixed-methods study shows a new model improves ethical knowledge in students, highlighting needed advancements in AI ethics education.
The rapid integration of artificial intelligence (AI), particularly generative AI (Gen-AI), into higher education presents a critical challenge: preparing students for the complex ethical dilemmas inherent in AI-driven research and practice. Current AI ethics education, however, often remains fragmented, overly theoretical, and disconnected from practical application, leaving a significant gap between knowing ethical principles and acting upon them. To address this pressing issue, this study proposes and validates a full-cycle AI ethics education system designed to bridge this gap. The system integrates three core components: (1) an updated four-dimensional ethics framework focused on Gen-AI challenges (research review, data privacy, algorithmic fairness, intellectual property); (2) a “cognition-behavior” dual-loop training mechanism that combines theoretical learning with hands-on, simulated practice; and (3) a full life-cycle education platform featuring tools like virtual laboratories to support experiential learning. A mixed-methods study with 360 students and 20 instructors demonstrated the system’s effectiveness, showing significant improvement in students’ ethical knowledge, a large effect size in enhancing ethical decision-making capabilities, and high user satisfaction. These findings validate a scalable model for AI ethics education that moves beyond passive instruction toward active, situated learning, offering a robust solution for higher education institutions to cultivate ethical responsibility in the age of Gen-AI.
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Xu et al. (2025) studied this question.