Taking 200 foreign articles on AI-enabled English learning from the Web of Science Core Collection database published between 2015 and 2025 as research samples, this study adopts CiteSpace visualization software to conduct a systematic visual analysis from the dimensions of annual publication volume statistics, keyword co-occurrence, keyword burst detection, keyword clustering analysis and so forth. The results reveal that the overall development of this field presents a trend of initial stagnant growth followed by explosive expansion. Driven by the emergence of generative artificial intelligence since 2022, the annual publication output has grown exponentially. A comprehensive research system has been constructed centering on generative AI, English language learning, and human-robot co-learning. Furthermore, research frontiers have gradually shifted toward practical teaching implementation, learner psychological intervention, and intrinsic studies of second language acquisition. Among them, learner language anxiety and second language acquisition remain persistent hotspots at present. By systematically sorting out the developmental trajectory and overall research landscape of the field, this study identifies existing research gaps, so as to provide references for subsequent academic research and teaching practice in AI-enabled English learning.