Radio Galaxy Zoo: Evolutionary Map of the Universe (RGZ-EMU) is the latest development of Radio Galaxy Zoo, an international citizen science project associated with the IAU OAD and SKAO communities. In the era of the Square Kilometre Array (SKA) and Artificial Intelligence (AI), RGZ-EMU addresses a key challenge in radio astronomy: identifying, classifying, and cross-matching millions of radio sources across multiple wavebands to build reliable value-added catalogues. In this talk, I will introduce how RGZ-EMU operates as a human-AI citizen science platform, showing how citizen scientists, professional astronomers, educators, and machine-learning tools contribute to the discovery workflow. I will present early science results, discuss how volunteers learn about radio galaxies through source identification, and briefly describe how the platform supports astronomy education and public engagement.