A Survey of Human-AI Collaboration for Scientific Discovery

Artificial intelligence (AI) is increasingly integrated into scientific discovery processes, such as protein design, gene analysis, and materials research, significantly improving research efficiency in these fields. While much of the recent literature emphasizes fully automated pipelines, it is crucial to acknowledge that scientific discovery is inherently a creative and high-stakes endeavor. Therefore, it relies heavily on human expertise for judgment and guidance, especially in the face of uncertainty. Despite the rapid growth in human-in-the-loop and collaborative systems, the field still lacks a unifying survey that explains how humans and AI collaborate throughout the scientific discovery life cycle. In this paper, we present a systematic review of human–AI (HAI) collaboration for scientific discovery. Specifically, we identify four representative roles that humans and AI can assume, including Informer, Explorer, Evaluator, and Controller. Using this lens, we then distill common HAI collaboration patterns across three distinct stages in the scientific discovery process (i.e., observation, hypothesis, and experiment). Finally, we identify key gaps in existing approaches and outline future research directions for developing trustworthy, role-aware HAI collaboration systems in scientific discovery.