AI Ethics Education and Evaluation Framework Based on Case Method Using Network Motif and Sentiment Analysis

While AI plays an expanded role in areas of significant decision making, the significance of pedagogical models that both promote moral understanding and emotional sensitivity is heightened. To address the challenge, this paper posits the new pedagogical model termed the AI Ethics Education and Evaluation (AEEE) model. It utilizes network motif analysis, measurement of emotion, and case-based teaching to take account of the overall dimension of moral growth. The paradigm was first applied in the Japanese job market, in which algorithmic screening techniques present culturally and ethics-specific concerns. The model has three stages that are cumulative. It subjects the student to real-world moral dilemmas, takes account of long-term evolution of the structure of discourse with patterns of motifs, and observes the change of emotion with sentimental quantities. Graduate student findings demonstrated an increase in the number of motifs that signal intellectual agreement and an overall brighter emotional tone as well. These signify that the use of the AEEE model is not simply arousing the individual to think more critically of ethics, but that it helps in their relationship with their own emotions as well in an improved way. This approach helps with an expanded assessment of AI ethics pedagogy and its broader societal consequences with the provision of an approach.