AI-Assisted Scientific Research

The field studies how AI can help across the research process, from finding and synthesizing literature to proposing ideas, analyzing data, writing code, and planning experiments. Common approaches include retrieval-augmented language models, knowledge graphs, tool-using multi-agent systems, and domain-specific models for areas such as chemistry, biology, and astronomy. Evaluation ranges from literature and coding benchmarks to reproducibility tests and prospective laboratory or field studies. AI is often useful for structured, tool-mediated tasks, but open-ended discovery, reliable screening, calibrated uncertainty, and faithful experimental validation remain difficult, making provenance and human oversight central.