Spectro represents a significant advancement in chemistry by tackling the complex challenge of molecular structure elucidation without relying on prior structural information. This innovative framework, independent of extensive preprocessing pipelines, is highly adaptable and can be extended to various spectroscopy techniques, thereby broadening its applications in chemical analysis. In self-driving laboratories (SDLs), Spectro could automate the characterization of molecules and provide deeper insights into the structures of synthesized compounds, enhancing the computationally guided platform. Furthermore, in educational settings, Spectro can support undergraduate students by aiding their understanding of molecular structure elucidation, a crucial but complex skill. Approaches like Spectro could enrich both research and educational environments. A potential limitation of Spectro is its reliance on automated pipelines without the existence of a validation scheme, which may mischaracterize chemicals in SDLs and hinder the development of essential structure elucidation skills in future chemists. However, this limitation can be mitigated with the increasing data being generated by SDLs.