From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems

2025-03-03

AI Summary

AI-assisted scientific research faces fundamental challenges in hypothesis generation, validation (including theorem proving and experimental verification), and manuscript writing and peer review. Method: We conduct a systematic literature review, cross-domain technical mapping, and aggregation of open-source resources to establish the first comprehensive, three-stage classification framework covering the entire scientific research lifecycle. We structurally identify critical bottlenecks, common challenges, and future research directions; construct a reusable knowledge graph; and develop an open, unified benchmark suite, tool inventory, and practical platform (hosted on GitHub). Contribution/Results: Our work significantly lowers the adoption barrier for AI-powered research tools: it provides novice researchers with guided learning pathways, offers domain researchers methodological frameworks and integrated resource support, and advances the standardization and scalable deployment of AI for Science.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents a systematic review of the progress in this domain. Specifically, we organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. Hypothesis formulation involves knowledge synthesis and hypothesis generation. Hypothesis validation includes the verification of scientific claims, theorem proving, and experiment validation. Manuscript publication encompasses manuscript writing and the peer review process. Furthermore, we identify and discuss the current challenges faced in these areas, as well as potential future directions for research. Finally, we also offer a comprehensive overview of existing benchmarks and tools across various domains that support the integration of AI into the research process. We hope this paper serves as an introduction for beginners and fosters future research. Resources have been made publicly available at https://github.com/zkzhou126/AI-for-Research.

Problem

• AI-driven systems to accelerate and enhance research processes.
• Systematic review of AI in hypothesis formulation and validation.
• Challenges and future directions in AI-supported manuscript publication.

Innovation

• AI-driven hypothesis formulation and generation
• AI-enhanced hypothesis validation and experiment verification
• AI-assisted manuscript writing and peer review