Sanja Todosijević
- AI Academy, Otona Župančiča 4, Belgrade, 11070 Serbia
Igor Razbornik
- Smart Institute, Šalovci 187, Slovenia
Saša Jovanović
- Supernova Center for Education, Research and Training, Otona Zupancica 34, 11070 Serbia
Abstract:
Institutions in higher education (HE) face systemic barriers to securing competitive research funding (e.g., Horizon Europe and Erasmus+), driven by fragmented systems and a critical AI alignment gap that neglects strategic grant evaluation. This paper introduces the Win-Code Predictive System, the first comprehensive AI mentorship framework designed to address these failures. Win-Code reframes AI as a strategic mentorship partner, guiding research teams through an evaluation-aligned 26-step methodology. The architecture, which includes the SCORE™ predictive quality assurance engine, ensures proposal content aligns with funder criteria. A three-year validation study on Erasmus+ proposals demonstrated significant efficacy, reducing proposal development time from a 30-day baseline to just four days (an 85% saving). Users achieved a first-submission acceptance rate more than double the EU average of 26%, maintaining a 61% success rate by 2025 in a hyper-competitive environment. Win-Code offers a replicable blueprint for institutional innovation, positioning the university as an AI-Powered Project Hub.
International Scientific Multidisciplinary Conference: AI for a Smarter Tomorrow - AI-SMART , September 25-26, 2025
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