Education Research: Validation of an AI-Powered Educational Platform for Neuroanatomical Localization and Stroke Syndrome Recognition

Background and Objectives: Neuroanatomical localization is foundational to stroke practice but remains difficult across training levels. We evaluated whether LouiS, an AI educational platform using retrieval-augmented generation over a curated stroke-syndrome knowledge base, improves the proportion of correct responses for neuroanatomical localization and stroke-syndrome recognition, with a focus on its potential as a teaching aid. Methods: This two-phase study recruited 80 participants from 3 Brazilian university-affiliated centers. Phase 1 used web-app allocation with browser-fingerprint persistence to keep each participant in the same test condition, with or without LouiS, across repeated access. Phase 2 used a within-participant paired design in which 62 participants evaluated case-matched vignettes first without and then with LouiS. Participant-level scores used the actual number of scorable responses in each condition. The allocation protocol required exposure to higher-difficulty vignettes and supplemented them with randomly selected cases. The primary outcome was the proportion of correct responses against a consensus reference standard. Deviations clustered on differential-diagnostic pairs. Discussion: Use of LouiS was associated with improved performance in neuroanatomical localization and stroke-syndrome recognition during case-based testing. The findings support LouiS as an educational aid for neuroanatomical reasoning and continuing medical education. Knowledge retention, behavior change, and real-world clinical outcomes require separate prospective evaluation.