The ability of artificial intelligence to shoulder a significant portion of the workload in grant applications is indicative of a system that seems increasingly outmoded. It’s high time we simplify the process for scientists seeking research funding.
Let’s face it, grant writing can be an ordeal.
As scientists, we thrive on ideation, on sketching diagrams and articulating our thoughts through words. However, the process of applying for grants requires a mountain of work that extends way beyond merely communicating an idea for a research project. This is a time-consuming and labor-intensive endeavor.
Typically, grant applications require you to present a standard case for support, outlining your proposed research. But, as any seasoned researcher would attest, there are often several other elements involved. These could range from a lay summary, a long abstract, your CV, impact statements, public-engagement plans, to detailed explanations of staff involvement, project management plans, letters of support from colleagues, data handling strategies, and the projected timeline of the project. And let’s not forget the risk analysis! All this effort, only to face a 90-95% probability of rejection.
Despite the extensive preparation, the harsh reality is that once the research commences, things might not pan out as anticipated. Milestones might not be met, some projected outcomes may remain unrealized, and if experiments falter, you might not have the bandwidth to execute all the public-engagement activities outlined in the grant application. Yet, at the end of the project, you may still end up contributing significantly to scientific progress, even if the results diverge from your initial proposal. And that should be perfectly acceptable.
From the perspective of panel members tasked with awarding the grants, the process is far from seamless. Having served on panels myself, it’s clear that there simply isn’t always sufficient time to read through every application in detail. Panel members often concentrate on three main questions: Does the proposal align with the call brief? Is the proposed science sound and innovative? And are the applicants experts in their field? The abstract and a portion of the research proposal answer the first two questions, while a quick Google search can provide insights into the applicants’ expertise.
So, why do applicants need to produce such an extensive array of documents? The system is designed to be rigorous, robust, and devoid of bias; it’s intended to ensure that funding bodies receive serious proposals. The exhaustive nature of the process ensures that only truly dedicated individuals apply. However, the creation of these lengthy, seemingly redundant documents is consuming an inordinate amount of scientists’ time.
Enter ChatGPT, the artificial intelligence (AI) chatbot that is shedding light on the system’s shortcomings.
Recently, when discussing a grant proposal with a colleague, I mentioned my lack of time to draft the proposal as I had envisioned. He recommended using ChatGPT, which he uses to handle the more tedious aspects of grant applications.
I decided to give ChatGPT a try while working on another grant proposal. The abstract was ready, but I asked ChatGPT to elaborate on the core ideas I had noted down. The results were impressive, with the AI producing high-quality English text. I also used ChatGPT to explain how our proposed research aligned with the funder’s call. Again, the results were satisfactory. A few minor edits were necessary to mask the use of AI, but it reduced my workload from three days to just three hours.
Upon submitting the grant, I casually mentioned to a friend that I had just written my first “ChatGPT grant.” To my surprise, he revealed that he and many other scientists had been using AI for this purpose for months. A 2023 Nature survey of 1,600 researchers found that over 25% use AI to assist in manuscript writing and more than 15% use the technology for grant proposals.
While some may view using ChatGPT for grant proposals as a form of cheating, it underlines a larger issue: why are we asking scientists to produce documents that AI can easily generate? What additional value are we bringing to the table? Perhaps it’s time for funding bodies to reassess their application processes.
