The advancement of biological sciences has long been linked to technological progress. ChatGPT, a generative artificial intelligence chatbot, produces human-like conversational responses and offers potential support for research across diverse scientific disciplines. However, its utility in natural sciences, particularly botanical research, remains largely unexplored. This study systematically evaluates ChatGPT’s performance in plant identification, creation of taxonomic keys, morphological description analysis, distribution mapping, and image-based species recognition. Across multiple tests involving taxa from different regions, the chatbot frequently produced inconsistent or incorrect outputs, including misidentification of species, erroneous synonymy assignments, fabricated references, and non-functional visualisations. Performance was especially limited for closely related species, hybrids, and microphotographs, while genus-level identification and text refinement were comparatively more reliable. These findings highlight the risks of relying on ChatGPT for botanical research, particularly for students and early-career researchers, emphasising the necessity of critical verification. Despite current limitations, ongoing improvements in AI suggest that future versions may offer more consistent and accurate support in biodiversity studies.