ACM Computing Surveys· 2026Q1· Review
Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation
- 8citations
- Q1SCImago
- 2026year
Short summary
Large language models are poised to transform scientific research by assisting in literature search, idea generation, experimentation, content creation (text and figures), and evaluation (e.g., peer review).
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Key points
- LLMs can support scientific research across five core tasks: literature search, idea generation/experimentation, text generation, multimodal artifact creation, and evaluation.
- The survey covers representative techniques, evaluation practices, and emerging trends in AI-assisted scientific discovery.
- It examines datasets, methods, results, evaluation strategies, limitations, and ethical considerations, including risks to research integrity.
- The goal is to provide an orientation for newcomers and act as a catalyst for AI4Science systems.
AI-generated from the title and abstract; the full text is not read.
Abstract
With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation. An emerging ecosystem of models and tools aims to support researchers throughout the scientific lifecycle, including (1) searching for relevant literature, (2) generating research ideas and conducting experiments, (3) producing text-based content, (4) creating multimodal artifacts such as figures and diagrams, and (5) evaluating scientific work, as in peer review. In this survey, we provide a curated overview of literature representative of the core techniques, evaluation practices, and emerging trends in AI-assisted scientific discovery. Across the five tasks outlined above, we discuss datasets, methods, results, evaluation strategies, limitations, and ethical concerns, including risks to research integrity through the misuse of generative models. We aim for this survey to serve both as an accessible, structured orientation for newcomers to the field, as well as a catalyst for new AI-based initiatives and their integration into future “AI4Science” systems.
The authors' abstract, as published at the source. ACM Computing Surveys, 2026 · DOI ↗
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Field: Information Systems and Management
Information Systems and ManagementDecision Sciences