In today’s class, I experimented with using Claude AI as a research assistant to locate scientific papers focused on pKa values of PFAS (per- and polyfluoroalkyl substances). While the AI successfully provided me with several relevant academic papers and resources, I discovered that the results weren’t precisely aligned with my specific research needs.

This experience taught me an important lesson about effectively utilizing AI tools for academic research. I realized that the quality of results directly correlates with the quality of my input. When working with AI systems like Claude, having baseline knowledge of your subject matter is essential, as it enables you to formulate precise, targeted queries.

The more specific and technically accurate my questions became, the more relevant the responses were. I found that breaking down complex research questions into smaller, more focused inquiries yielded better results than broad, general requests. This approach allowed the AI to identify and retrieve the most pertinent information from scientific databases and publications.