As artificial intelligence (AI) technologies become more deeply integrated into everyday life, the ethical challenges they pose—from algorithmic bias and privacy breaches to questions of accountability and societal impact—have gained attention. While there is existing research on how general or tech ethics is taught within computer science (CS) education, comparatively little is known about the specific treatment of AI ethics within CS education. This study addresses that gap through a large-scale analysis of 955 AI-related publicly available course syllabi from top U.S. universities, focusing on the presence and depth of instruction on AI ethics. Of these, only 83 courses incorporated AI ethics content. We analyzed these syllabi to assess the extent of coverage, instructional approaches, topics addressed, learning objectives, and assessment methods used in teaching AI ethics. Our findings indicate that AI ethics is most often integrated into broader AI-related technical courses rather than taught as a dedicated subject, with most courses allotting only one or two sessions for these topics. Commonly addressed themes include algorithmic bias, data privacy, transparency, and social impact. This work highlights both the progress and the gaps in preparing future technologists to engage with the ethical dimensions of AI, and it offers a framework for enhancing the integration of AI ethics in computing curricula.