Abstract
Generative AI is entering everyday newsroom work and changing how news is made, shared, and trusted. Tools that draft text, rewrite copy, and help produce multimedia can speed up output, but they also raise ethical problems around verification, attribution, accountability, privacy, and transparency. This study examines how these problems are being translated into journalism ethics education in Kazakhstan and China from 2018 to 2025. Using a comparative thematic review, we analyze publicly available regulations and policy notices, selected media cases, industry, fact-checking materials, and university documents such as program descriptions, syllabuses, and course outlines. The findings show two routes of adaptation. In Kazakhstan, visible cases such as deepfakes and misinformation have pushed gradual, practice-based change in newsroom routines and teaching tasks. In China, change is driven more by formal rules that require disclosure, labeling, basic records, and human review for higher-risk content. In both contexts, ethics education is moving from rule learning to routine building that can be practiced and assessed. The trade-off differs, with unevenness on one side and rigidity on the other. Ethical judgment still matters when automated systems shape what audiences see and trust.