This study explores integrating generative AI to enhance citation context typing. Using Claude LLM, we generate synthetic data aligned with the Citation Typing Ontology (CiTO) to train a classifier. This supervised learning experiment involves training a classifier to identify citation types using this synthetic data. We evaluate the classifier’s performance on uncategorised citation statements. Additionally, we extend our analysis to test the classifier trained on English language citation context statements on statements extracted from Swedish and German research publications. A novel aspect of this work lies in the fusion of bibliometrics and experimental work in semantic modelling, employing language models to train machine learning models for research content evaluation. While acknowledging the inherent limitations of machine learning algorithms, we propose further testing using real-time scenarios and human evaluators. This study aims to push the boundaries of research methodology by integrating generative AI beyond text generation into the research process itself.