Can Generative AI Simulate a Skeptical Grant Reviewer?

Can AI Challenge Your Grant Proposal the Way a Critical Reviewer Might?

After spending considerable time developing a grant proposal, you may find it increasingly difficult to read the application as an unfamiliar reviewer would. You understand the background, know why the methodology was selected, and can explain the project's significance. Unfortunately, those explanations may not all be evident in the submitted document.

What if you asked generative AI to act as a skeptical grant reviewer rather than a supportive writing assistant?

The model could question your assumptions, challenge the feasibility of your methods, and identify claims that appear insufficiently supported. Such an exercise might help you anticipate difficult questions before the proposal reaches a review panel.

Yet simulated skepticism has limitations. A model instructed to find problems may generate criticisms simply because criticism is expected, regardless of whether the objections are scientifically justified.

The useful question is not whether AI can sound like a demanding reviewer. It is whether the resulting critique can improve the proposal without distorting its scientific purpose.

Yes, but a Simulated Reviewer Is Not an Actual Peer Reviewer

How a Skeptical AI Grant Review Can Help, and Where It Can Mislead

What Does It Mean to Simulate a Skeptical Grant Reviewer?

A skeptical reviewer simulation is an exercise in which a researcher asks an AI system to examine a grant application from a deliberately critical perspective.

Rather than asking the model to improve the writing or praise the proposal's strengths, the researcher asks it to identify assumptions, question the evidence, and examine whether the proposed work can deliver its stated objectives.

This resembles a form of adversarial review, in which an argument or research design is deliberately challenged to expose weaknesses before an important decision.

However, a simulated review is not equivalent to formal peer review. The model has not necessarily developed the disciplinary expertise, contextual understanding, or independent judgment required to assess the proposal as a qualified reviewer would.

Its criticism is generated from the available information and the instructions supplied to it. The result should be treated as a structured stress test rather than a prediction of the funding decision.

Why Skepticism Can Be More Useful Than General AI Feedback

When researchers ask AI whether a proposal is good, the system may provide encouraging but relatively superficial feedback.

A more demanding instruction can redirect attention toward unresolved questions.

For example, a proposal may claim that an intervention will improve student learning, but its evaluation plan may measure only user satisfaction. A skeptical review could challenge the relationship between the intended outcome and the evidence being collected.

Similarly, a project may propose ambitious national policy recommendations based on data collected from one institution. A critical reader might question whether the proposed evidence supports that scope of inference.

The value of these questions is that they force the applicant to defend the relationship between claims, methods, and evidence.

Nevertheless, a skeptical tone does not guarantee analytical quality. The model must still provide a defensible reason for each criticism.

Use the Funding Agency's Actual Review Criteria

A convincing simulation should reflect the evaluation framework of the intended funding program.

For example, the US National Science Foundation (NSF) evaluates proposals using intellectual merit and broader impacts, with additional criteria where specified by the solicitation. The National Institutes of Health (NIH) uses a simplified review framework for many research project grant applications, organizing scientific merit assessment around the importance of the research, rigor and feasibility, and expertise and resources.

These are established examples of formal review frameworks, not universal standards for every funding organization.

For NIH applications covered by the simplified framework, the first two factors receive numerical criterion scores, while expertise and resources are assessed for sufficiency. A generic simulation that assigns independent numerical scores to all three factors would therefore misrepresent that framework.

Researchers should provide the current funding call and review criteria rather than asking AI to invent a generic scoring system. Correct interpretation of the funding announcement is essential before attempting a realistic mock review.

Which Questions Should a Skeptical Reviewer Ask?

The most useful questions are those that challenge the proposal's central scientific argument rather than focusing exclusively on wording.

The questions should be adapted to the study design. A qualitative investigation, exploratory project, randomized trial, and policy evaluation may require different forms of scrutiny.

Ask for Evidence-Based Criticism Rather Than Maximum Negativity

There is an important difference between asking AI to be rigorous and asking it to be relentlessly negative.

A prompt such as "Find as many weaknesses as possible" may encourage the model to generate numerous speculative objections, including concerns that are already addressed or do not apply.

A stronger approach requires the model to identify the relevant proposal passage, explain the concern, state why it matters under the funding criteria, and describe what additional evidence would resolve it.

It should also be allowed to conclude that a particular aspect of the proposal is adequately justified.

This reduces the pressure to manufacture criticism and makes the review more useful for actual revision.

Can AI Simulate Different Types of Grant Reviewers?

AI can be instructed to examine a proposal from different evaluative perspectives, provided these perspectives correspond to the funding process.

For example, a methodological reviewer might concentrate on research design and analysis, while a disciplinary specialist might focus on the literature and scientific contribution. A reviewer concerned with implementation may question institutional capacity, costs, and project management.

Such perspectives can reveal different kinds of uncertainty, but they should not be mistaken for independent expert judgments.

Multiple simulated reviewers generated by the same model may share underlying assumptions and reproduce similar errors. Their apparent agreement does not establish that a criticism is valid.

Furthermore, actual review panels may contain expertise and priorities that the simulation cannot anticipate.

Can AI Predict Reviewer Scores or Funding Success?

Not reliably.

An AI system may assign numerical ratings to a proposal, but those ratings do not automatically correspond to the scores that qualified reviewers would provide.

Formal funding decisions can depend on reviewer assessments, program priorities, available budgets, portfolio considerations, and administrative eligibility.

NSF, for example, explains that its funding decisions incorporate expert review alongside broader portfolio considerations. This means even a favorable review is not equivalent to a funding guarantee.

AI-generated scores may be useful as organizational devices when clearly labeled as hypothetical. They should not be interpreted as calibrated probabilities of success.

Why AI May Favor Conventional Research Designs

A skeptical AI review may disproportionately question approaches that differ from familiar methodological patterns.

For instance, an AI model might criticize an exploratory qualitative project for lacking hypotheses, or recommend a randomized controlled trial for a research question that is primarily interpretive.

Such recommendations may reflect common methodological examples rather than the most appropriate approach to the research problem.

This creates a concern that repeated AI-assisted review could encourage applicants to favor familiar designs and conventional language over scientifically justified alternatives.

The possibility of AI favoring conventional grant proposals over unusual research ideas should therefore be considered when evaluating its criticism.

A genuinely innovative proposal may need a stronger explanation of its rationale, but novelty alone should not be treated as a methodological defect.

Can AI Simulations Replace Feedback From Human Colleagues?

No. A simulated review can complement feedback from colleagues, mentors, methodological specialists, and institutional research support offices.

Human reviewers may recognize disciplinary developments, practical constraints, or institutional circumstances that are not adequately represented in the AI's information.

They can also distinguish between a risky but potentially important research idea and a poorly justified design.

AI is most useful when it helps researchers prepare more focused questions for expert consultation rather than encouraging them to avoid human scrutiny.

Confidentiality and Funding Rules Still Apply

Before submitting an unpublished grant proposal to an AI system, researchers should check the service's data practices and the requirements of their institution and funding organization.

Proposals may contain confidential partnerships, unpublished discoveries, preliminary findings, personal information, or commercially sensitive material.

Researchers should also verify whether the funding program restricts or requires disclosure of AI assistance. The funder's rules concerning AI use should be established before using the tool.

Where the proposed upload is not authorized, a researcher may instead use nonconfidential descriptions or institutionally approved systems.

Simulating a Skeptical Review of an Educational Technology Grant Proposal

The AI produces the following critical questions:

The researcher evaluates each criticism against the actual proposal.

The concern about confounding is potentially substantive because the study compares existing classes. The researcher strengthens the design justification and clarifies what causal conclusions can reasonably be supported.

The implementation-fidelity question reveals an explanation that was missing from the written proposal, even though the research team had already developed an instructor training plan.

The policy-relevance criticism prompts the researcher to distinguish between recommendations for the participating institution and claims that would require evidence from a broader population.

Not every simulated criticism necessarily requires a methodological change. Some may be resolved through clearer documentation, while others may warrant a genuine redesign.

Misconceptions About Simulated AI Grant Reviewers

How to Conduct a Useful Skeptical AI Review Before Submission

A useful simulation should resemble a structured examination of the proposal rather than an exercise in generating negative comments.

Begin with the official funding criteria and ask the AI to assess the proposal against those criteria. Require it to identify specific evidence, distinguish major concerns from minor issues, and acknowledge where the proposal already provides an adequate justification.

How Should You Decide Which Criticisms to Act On?

One practical strategy is to maintain a response-to-criticism document, recording each substantial objection and the researcher's decision. This can help distinguish unresolved problems from concerns that have been adequately addressed.

Researchers may also conduct a more general assessment of proposal weaknesses before using a skeptical reviewer simulation. The broader assessment can identify obvious omissions, while the simulation can challenge the reasoning behind the remaining design choices.

The goal is to make the proposal more defensible, not simply more conventional or more agreeable to an imagined reviewer.

Checklist for Simulating a Skeptical Grant Reviewer

Frequently Asked Questions About AI Grant Reviewer Simulations

A Skeptical AI Reviewer Can Challenge a Proposal Without Predicting Its Fate

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