Differentiation between AI and human responses to research ethics and integrity cases– implications for teaching

The current study investigates the differences between AI-generated and human responses to ethical analyses in research ethics and integrity education, using the SOLO taxonomy to evaluate the depth of understanding. As generative AI models increasingly produce human-like responses, concerns arise regarding their impact on academic integrity and critical thinking. The findings reveal that while AI can achieve multistructural and relational levels of understanding, human responses, particularly from experts, consistently demonstrate higher levels of critical thinking and integration of ethical principles. AI responses often lack the depth and novelty characteristic of human analyses, particularly in proposing innovative solutions and considering broader implications. This suggests that reliance on AI in ethics education may hinder the development of essential skills needed for academic and professional success. The study also highlights the potential for AI to serve as a tool for evaluating student responses to learning assignments, provided transparency is maintained. Overall, educators are encouraged to design assessments that foster independent thought and creativity while integrating AI responsibly into the learning process. Future research should explore the long-term effects of AI on ethics competencies and the effectiveness of various AI platforms in educational settings.