proposal - chunhualiao/public-docs GitHub Wiki

AI-Powered Automation of Research Proposal Writing and Review in STEM

Challenges

lack public large-scale datasets of proposals because proposals are usually confidential.

Ensuring factual accuracy and proper grounding in domain knowledge is a fundamental challenge.

matching funding agency or program managers' needs

assembling the right team

A compelling proposal needs to present an innovative idea that is not just a rehash of existing work.

Research proposals are highly domain-specific documents, often filled with technical jargon, formulas, and shorthand understood only by experts in that field. A generic language model might not generate the appropriate terminology or tone expected by reviewers in, say, a quantum physics proposal or an NIH biomedical grant. Domain adaptation is a challenge

style and conventions: Each community has unwritten rules about how proposals should be phrased. For example, NIH R01 proposals often have a very structured style (Significance, Innovation, Approach sections) and a certain formal tone, whereas an AI that injects marketing language or unsupportable hype would be frowned upon. Ensuring the model respects these conventions is challenging. It requires either prompt engineering (providing exemplars of the style) or fine-tuning on a collection of well-written proposals in that style (which are often scarce due to confidentiality). If the model isn’t properly adapted, the generated text may stick out as “AI-ish” or simply not discipline-appropriate.

Research proposals are typically long documents (e.g., 10-15 pages single-spaced for a federal grant) with many sections that must all cohere into a single narrative. There is a need for high-level document planning: the aims stated in the introduction must align with the experiments described later; the literature gaps identified must be addressed by the approach; the conclusion must tie back to the objectives, etc. Large language models, however, have limitations when generating long texts. Even with a large context window, generating a lengthy document in one go often leads to drift or incoherence – the model might forget details from earlier, introduce inconsistencies, or meander off-topic.

AI tools

HyperWrite's Proposal Generator – HyperWrite (a commercial AI writing platform) offers a specialized Grant Proposal Generator.

HyperWrite's Proposal Generator – HyperWrite (a commercial AI writing platform) offers a specialized Grant Proposal Generator. It can take a brief description of the project idea and expand it into a detailed proposal draft, covering sections like problem statement, objectives, and methodology.

Grantable – A dedicated AI grant writing software focused on automating proposal drafting for funding applications. Grantable is designed specifically for grants (academic and nonprofit) and acts as a writing environment with an AI assistant built in (Grantable: The world's best grant writing tools). Notably, Grantable lets users upload or curate a library of their own content (past proposals, boilerplate text, etc.) which the AI can draw upon

There are numerous other tools in this space:

  • FundWriter.ai which auto-fills proposal templates from fielded inputs,
  • Grant.io (an AI platform for effective grant writing)
  • Granted AI which is “specifically trained for grant proposal writing”
  • LogicBalls Research Proposal Generator. These vary in sophistication; some simply wrap a GPT-4 API with a prompt template, while others incorporate training on successful proposals.
  • Texta.ai, a general AI writing assistant, is also advertised for grants, claiming to generate “ready-made documents with accurate information” to speed up writing

Emerging Techniques

several key solution strategies:

  • (a) retrieval-augmented generation for grounding, RAG
  • (b) expert-in-the-loop and human feedback architectures,
  • (c) prompt engineering and fine-tuning methods to guide models,
  • (d) human-AI collaborative workflows for iterative refinement, and
  • (e) AI agents with tool use and planning capabilities.

prompt engineering