Values in Education: Exploration of Artificial Intelligence Ethics Syllabi Using Natural Language Processing Analyses

With new technologies come additional responsibilities. Examining Artificial Intelligence (AI) through an ethical lens has become increasingly important and significant. Advancements in AI have led numerous organizations, such as IEEE, to develop AI ethics guidelines for consideration in academia and industry. Additionally, higher education has an essential role in fostering innovation and developing skilled professionals who will work on topics that span social, philosophical, scientific, and technical spheres. To assess the content being covered in tertiary classrooms, we utilized a Natural Language Processing (NLP) approach for analysis. This study examines <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{n}=45)$</tex> AI ethics syllabi that were publicly available online. The course description, topics, department, and year were some important features captured from each syllabus. Using various NLP tools for analysis, a general exploration of AI ethics curricula was conducted. Through supervised clustering, k-means clustering, and latent Dirichlet Allocation (LDA), various patterns in the contents of the AI ethics syllabus were found. Some of these include trends and patterns from syllabi across various academic departments, years, and the pre-post Chat-GPT era. Cluster evaluation was also done on the unsupervised clusters using various metrics to determine the viability of the clusters. The LDA analysis enabled a review of topics that are consistent among the clusters, which helped highlight salient areas of focus in AI ethics syllabi. The findings from this study can serve to inform administrators and educators, acting as a baseline for including language around AI ethics topics and uncovering potential topical gaps in the contents of AI ethics syllabi. They can also provide insight into how different academic departments, like computer science and philosophy, may approach the topic. Such understanding is critical to ensuring the next generation of graduates not only considers how to utilize AI but also promotes doing so responsibly and with regard to its societal implications