The use of artificial intelligence (AI) for audiovisual dubbing has become increasingly popular due to its ability to improve content production and dissemination. In particular, the application of machine translation (MT) to audiovisual content has resulted in more efficient and productive AI content generation. To assess the quality of MT-dubbed videos, this study proposed the use of the new FAS model. This model adapts the FAR model put forward by Pedersen (2017), with the “R (Readability)” parameter replaced with “S” to include synchrony: this amendment responds to research on dubbing quality that identifies the need to explore better methods for synchronization between audio and video. Using Mulan (Caro, 2020) – an English-language Disney feature film released in 2020 – as a case study, this article evaluates the quality of automatically dubbed videos generated by YSJ (Ren Ren Yi Shi Jie), an MT platform for audiovisual products in China. By analysing errors in functional equivalence, acceptability, and synchrony, the study assesses whether China’s latest MT engine can meet the demand for quality dubbing and improve cross-cultural communication. The findings show that whereas China’s present MT platform can generate a moderately acceptable result, there are still semantic errors, idiomaticity problems, and synchrony errors which may lead to incorrect translations and consequently possible misunderstanding by viewers. Overall, this study sheds light on the state of AI-dubbing technology in China and highlights areas for improvement.