|
HEALTH AND AI RESEARCH GROUP: STRUCTURE, CHALLENGES, AND TEACHING PRACTICES
Background / Aims
• Research group founded in 2016 with face‑to‑face meetings and slow individual onboarding�• In 2025 the structure transitioned to a remote collaborative model�• Seven new members entered simultaneously�• Aim: evaluate whether AI‑supported digital supervision accelerates research training while maintaining quality
Materials and Methods
• Supervision performed entirely via WhatsApp using calls and messages�• Weekly checkpoints structured research workflow�• Members organized in pairs or trios�• AI tools used for idea organization, summaries / structuring manuscripts
Results and Discussion
• Research learning curve accelerated during collaborative activities
• Thematic consistency across projects was maintained
• Intergenerational collaboration improved: experienced x new researchers
• Operational challenges: notification overload / coordination of tasks
• AI improved efficiency and scalability of supervision processes
• Human mentoring remained essential to ensure conceptual coherence and academic maturity
Conclusion
The combination of AI-supported organization and human supervision proved effective in expanding research capacity and strengthening collaborative learning in health research and education. Future steps include testing hybrid onboarding models combining collective training and intergenerational supervision.
AbsRef: 2173
Estêvão Cubas‑Rolim¹, Gabriela Souza Santos Ribeiro, Elisângela Da Silva Lima, Miguel Cardoso, Joyce Lourenço Antero de Oliveira, Lucas Oliveira Da Silva, Juliana Coutinho Cabidelli, Lucas Carvalho Souza Esum
¹Department of Medicine, University of Brasília (UnB), Brasília, Brazil
www.escoladepacientes.com