AI ethics education: A scoping review of pedagogy, curriculum, and assessment

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.