Research Idea Generation

The field studies how AI can help researchers turn literature, datasets, and domain knowledge into useful, testable questions and proposals. Common approaches combine language models with literature retrieval, citation or knowledge graphs, structured reasoning, interdisciplinary analogy, and agent-based or evolutionary search; some systems also use human feedback or reinforcement learning to refine ideas. Evaluation ranges from expert comparisons and novelty or feasibility rubrics to tests of whether ideas anticipate later publications or lead to working experiments. A central tension is that ideas can sound novel while being poorly grounded, and systems that produce plausible extensions may still explore a narrower range of directions than human researchers.