Inside the funding process: Using generative AI to assess reviewers’ criteria prioritisation in multi-stage application assessments
Peter Kolarz, Diogo Machado · fteval Journal for Research and Technology Policy Evaluation · 2025
When evaluating funding schemes with multiple aims (expressed through multiple assessment criteria such as quality, novelty, relevance, collaboration, etc.), there is an inherent challenge in assessing what role different assessment criteria play in the selection and awarding process. As part of a process evaluation of the Austrian FWF’s Emerging Fields programme, we used generative AI to analyse peer reviewers’ reports on applications submitted to the scheme. The purpose of this analysis was to understand how various assessment criteria were being operationalised in the review process. Specifically, the Emerging Fields scheme has two separate written application assessment stages: a short outline-proposal stage, followed by a full application review stage. Background research on the scheme’s design led us to a hypothesis that reviews in the first of these two stages should emphasise and reward innovative potential and novelty of the proposed project ideas, while reviews in the second stage should place a greater emphasis on scientific quality of the research plans.