From Theoretical Navigation to Intelligent Prevention
Constructing a Full-Cycle AI Ethics Education System in Higher Education
Bibliographic Data
| ID | 22048734 |
|---|---|
| Authors | Xingjian Xu (0000-0003-0850-7896, Inner Mongolia Normal University, corresponding author), Fanming Meng (0000-0003-0264-1015, Inner Mongolia Normal University), Fanjun Meng (0000-0002-5934-1462, Inner Mongolia Normal University), Yan Gou (0000-0001-7570-7770, Inner Mongolia Normal University) |
| Year | 2025 |
| Volume | 15 |
| Issue | 9 |
| Pages | 1199 |
| Publication date | 2025-09-11 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci15091199 |
| OpenAlex | W4414124206 |
| Language | EN |
| References cited | 23 |
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
Applied ethics · Computer ethics · Experiential learning · Higher education · Information ethics · Professional ethics · Scalability · Situated · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Law, AI, and Intellectual Property
Moral Machines
Cognitive Load Theory
Value Sensitive Design
E‐Learning and the Science of Instruction
ChatGPT and a new academic reality
A meta systematic review of artificial intelligence in higher education
Big Data's Disparate Impact
The global landscape of AI ethics guidelines
The Fair Guiding Principles for scientific data management and stewardship
AI4People—An Ethical Framework for a Good AI Society
Exploring the impact of artificial intelligence on higher education
Reassessing academic integrity in the age of AI
Gender Differences in the Use of Generative Artificial Intelligence Chatbots in Higher Education
Bridging moral cognition and moral action
Systematic review of research on artificial intelligence applications in higher education – where are the educators
Situated Learning
| Highly cited | No |
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