Background Artificial intelligence (AI) is increasingly embedded in social and institutional decision-making, creating demand for ethically literate practitioners. Universities have responded by introducing AI ethics instruction, but the structure, content, pedagogy, and evaluation of these efforts remain unevenly documented. Objective To map and synthesize research on university level AI ethics education by characterizing course design, pedagogy, ethical themes, and assessment methods, and identifying evidence gaps that limit knowledge consolidation and instructional refinement. Methods We conducted a scoping review using Continuous Active Learning to screen 50,766 records up to 2024. 43 studies met inclusion after title, abstract, and full text review. We coded instructional design, curricular themes, pedagogical methods, and evaluation approaches using descriptive frequency counts and qualitative synthesis. Results Most included studies were conceptual or descriptive, with relatively few empirical evaluations. Instruction was concentrated in computing and engineering and primarily targeted undergraduate learners. Ethics content was more often embedded within technical courses than delivered as standalone offerings. Reported pedagogy relied heavily on lecture and case-based discussion, with fewer studies describing participatory formats such as simulations or role-play. Curricular emphasis clustered around bias/fairness and privacy, with comparatively less attention to governance, explainability, and trust. Evaluation most often relied on self-report and reflective methods, while validated instruments and performance-based assessments were less common, and behavioral or applied outcomes were rarely assessed. Conclusions The literature suggests a field oriented toward awareness-building more than measurable ethical competence. Clearer competency claims, stronger assessment transparency, and greater alignment between instructional design and evaluation would improve comparability across studies and support evidence-informed course development.

This special day event on Responsible Artificial Intelligence (AI) brings together researchers, practitioners, and policymakers to explore how data mining and machine learning systems can be designed to align with ethical principles, societal values, and human well-being. As AI technologies increasingly influence decisions in healthcare, finance, governance, and social systems, there is a critical need to develop frameworks that embed fairness, accountability, and privacy directly into the foundations of knowledge discovery. This full-day event will feature a mix of invited talks, interactive debates, expert panels, and peer-reviewed research presentations, all focused on the practical integration of ethical design into data-driven systems. The Responsible AI Day builds on the success of Canada's NSERC CREATE Program on Responsible AI, an interdisciplinary initiative training the next generation of AI researchers across computer science, law, bioethics, public health, and media studies. Topics will span scalable AI governance, privacy-preserving computation, algorithmic bias mitigation, and the socio-legal tensions emerging in generative AI. By positioning responsible AI as a sociotechnical challenge, this special day aligns with KDD's mission of advancing data science that is not only technically robust but also socially conscious.