Can Generative AI Favor Conventional Grant Proposals Over Unusual Research Ideas?

Could AI Grant Reviewers Prefer Familiar Research Ideas Over Unconventional Ones?

A researcher proposes an unconventional approach to a longstanding scientific problem. The methodology is carefully developed, but the underlying theory challenges established assumptions. Another applicant proposes a familiar approach using widely accepted methods and a well-established theoretical framework.

Both proposals may be scientifically defensible. Yet an AI system asked to assess their quality might find the conventional proposal easier to recognize as credible, feasible, or methodologically sound.

This possibility raises a consequential question for research funding: could generative AI systematically favor proposals that resemble established scientific practice while undervaluing unusual ideas that might advance a field?

The concern is not simply whether AI can identify novelty. It is whether AI-assisted evaluation might discourage the kinds of intellectual departures that research funding sometimes exists to support.

Yes, and Emerging Evidence Suggests the Risk Deserves Attention

Why AI-Assisted Grant Evaluation May Disadvantage Scientific Novelty

Scientific Novelty and Scientific Quality Are Not the Same Thing

Scientific novelty concerns the extent to which a research proposal introduces new questions, theoretical perspectives, methods, combinations of knowledge, or approaches to an unresolved problem.

Scientific quality concerns whether the proposed research is conceptually coherent, methodologically justified, and capable of producing credible evidence.

A proposal can be novel but scientifically weak. It can also be methodologically rigorous while contributing little beyond established knowledge.

Research funders must therefore consider novelty alongside other relevant criteria rather than treating unconventionality as either an automatic advantage or an inherent defect.

These dimensions can overlap, but they should not be collapsed into one judgment. An unusual methodology may require additional justification without necessarily being inferior to a conventional one.

Language Models Learn From Existing Patterns of Knowledge

Large language models are trained on extensive collections of existing information. Their training objectives generally encourage the prediction of plausible continuations based on patterns learned from that material.

This does not mean that language models are incapable of producing or recognizing unusual ideas. However, it creates a plausible concern that familiar scientific arguments, terminology, and methodological structures may receive more favorable assessments under certain evaluation conditions.

A proposal that closely resembles established scientific writing may align with patterns the model has frequently encountered. A genuinely unconventional proposal may require the evaluator to consider an unfamiliar combination of assumptions or methods.

Importantly, the relationship between next-token prediction and conservative grant evaluation is a hypothesis about possible mechanisms. It should not be presented as a demonstrated explanation for every observed preference toward conventional research.

What Does Empirical Research Show About AI Bias Against Scientific Novelty?

A particularly relevant study by Diogo Machado, published in Scientometrics in 2026, examined whether generative AI-based funding decisions differed systematically from human evaluations of scientific novelty.

The study analyzed 139 research proposals submitted in 2021 to the Austrian Science Fund across mathematics, physics and astronomy, chemistry, and biology. The proposals had expert-assigned evaluations covering novelty, scientific quality, team capacity, and feasibility, together with actual funding decisions.

Using GPT-4o, the researcher simulated competitive funding selection through comparisons between proposals within the same field. The resulting AI-based funding decisions were then compared with the human funding outcomes.

The analysis found that higher human-assessed novelty was associated with greater disagreement between the AI and human funding decisions. More importantly, the direction of disagreement suggested that proposals considered highly novel by human reviewers were more likely to be selected by humans but not by the AI-based procedure.

In the study's conditional logistic regression analysis, a one-point increase in human-assessed novelty was associated with greater odds of disagreement, including after accounting for other assessed proposal dimensions.

These findings provide evidence of a possible conservative tendency in the specific AI evaluation procedure studied. They do not establish that all generative AI systems systematically reject novel research, nor do they demonstrate the underlying causal mechanism. The original study explicitly acknowledges its limited sample and context-specific design.

Why Conventional Proposals May Appear Stronger to an AI Evaluator

Several mechanisms could contribute to an AI system favoring familiar proposals. These mechanisms are analytically plausible, but their relative importance must be established empirically for a particular system.

These possibilities also connect to the reproduction of historical research funding patterns. A system designed to imitate past decisions may perform well at predicting conventional success while remaining poorly suited to identifying research that challenges those precedents.

Novelty Can Introduce Legitimate Uncertainty

Not every unfavorable evaluation of unconventional research constitutes bias.

Unusual research proposals sometimes involve untested assumptions, uncertain measurement strategies, or methods without established validation evidence. Reviewers may reasonably ask whether the applicant has identified these risks and developed credible ways to address them.

The critical distinction is between evaluating uncertainty and penalizing novelty itself.

A proposal should not receive a favorable score simply because it is unconventional. Equally, a proposal should not be disadvantaged merely because its approach differs from prevailing practice.

For funding programs explicitly supporting exploratory or high-risk, high-reward research, some uncertainty may be expected and consistent with the program's objectives.

Conventionality Bias Could Influence the Direction of Scientific Development

If an evaluation system systematically rewards proposals resembling existing research, its influence may extend beyond individual funding decisions.

Researchers could begin adapting their proposals to anticipated AI preferences. They might emphasize familiar frameworks, avoid unconventional terminology, or reduce the visibility of speculative but potentially valuable ideas.

Over time, widespread reliance on similar evaluation systems could contribute to narrower research agendas, particularly if applicants and evaluators respond to the same model-generated expectations.

This is a potential systemic consequence, not an established outcome across research funding systems.

The NIST Generative AI Profile identifies homogenization as a relevant generative AI risk. Although that framework does not specifically demonstrate grant-selection conservatism, its discussion of reduced content diversity and overreliance on AI outputs is pertinent to evaluating how AI might influence scientific variety.

AI Could Also Help Identify Novelty Under Appropriate Conditions

Generative AI is not inherently limited to reinforcing conventional research.

When appropriately validated, AI systems may help reviewers identify unusual combinations of research concepts, compare a proposal with established literature, or articulate how a proposed method differs from conventional approaches.

Such assistance should not be confused with reliable independent judgment about scientific originality. A model may overlook relevant prior work, misinterpret an unfamiliar concept, or incorrectly claim that an idea is unprecedented.

The question is therefore not whether AI should always favor or reject unusual proposals. It is whether a particular evaluation procedure can assess novelty without confusing unfamiliarity with poor scientific quality.

How Can Funders Test for Bias Against Unconventional Research?

Funding organizations can investigate this concern by evaluating AI systems against proposal collections that vary meaningfully in novelty while retaining independently assessed information about scientific quality and feasibility.

Useful tests may include comparing AI and expert novelty ratings, examining whether disagreement is associated with independently assessed originality, and testing whether unusual terminology or methodological framing changes evaluations without altering substantive quality.

Funders should also examine whether an AI system performs differently across disciplines. What counts as an unconventional approach in theoretical physics may differ considerably from novelty in educational research or public health.

Repeated evaluations are useful because a single AI response may not represent stable model behavior. However, consistency alone does not establish that the evaluation is valid or fair.

When an Unconventional Educational Technology Proposal Receives a Lower AI Score

The hypothetical scores are illustrative and do not represent findings from an actual funding competition. The example demonstrates why an AI-generated preference for familiar methods should be examined against the funder's stated objectives rather than accepted as evidence of superior scientific merit.

It also illustrates a practical distinction: reviewers can reasonably question an unfamiliar methodology, but the absence of precedent is not itself proof that a research idea lacks value.

Misconceptions About AI, Novelty, and Research Funding

How to Protect Scientific Novelty in AI-Assisted Grant Evaluation

For researchers preparing unconventional proposals, the practical response is to explain originality precisely rather than assume that novelty will be self-evident.

Identify the established assumption your work challenges, explain why existing approaches are insufficient, and demonstrate how the proposed methodology can produce interpretable evidence. A strong justification helps both human reviewers and any permitted AI-assisted evaluation procedure assess the scientific rationale.

For research funders, protecting novelty requires more than adding an innovation category to a scoring rubric. The evaluation process must distinguish justified methodological uncertainty from avoidable design weaknesses.

Why Human Expertise Remains Important

Evaluating an unconventional research proposal may require expertise that goes beyond recognizing familiar methodological patterns.

Reviewers must consider whether an unfamiliar theory is coherent, whether a new method addresses a genuine limitation, and whether the potential scientific contribution justifies the associated uncertainty.

These judgments can be contested even among qualified experts. A funding process should therefore preserve opportunities for disciplinary debate, methodological challenge, and reasoned disagreement.

This is especially relevant to the boundary between AI assistance and human judgment in research funding.

Ultimately, an effective research funding system should not confuse conformity with scientific merit. Its evaluation procedures should be capable of recognizing rigorous conventional work while remaining open to defensible ideas that challenge established expectations.

Checklist for Assessing AI Bias Against Novel Research

Frequently Asked Questions About AI Bias Against Innovative Research

Scientific Novelty Should Not Be Penalized Simply Because It Is Unfamiliar

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