Engaging students in an AI-driven RNA drug design research project through a crowd science-infused learning approach.

Abstract

The rapid growth of RNA therapeutics and artificial intelligence (AI) has transformed antiviral drug discovery and created an urgent need for interdisciplinary training that links computational methods with authentic virology research. Despite this, undergraduate and postgraduate curricula rarely provide students with opportunities to contribute directly to an active AI-driven drug design research pipeline. To address this gap, we designed a unique 5-day, non-wet-lab workshop that immerses both undergraduate and postgraduate coursework students in an ongoing research project aimed at developing deep learning (DL) models for small interfering RNA (siRNA) antiviral drug design. Our workshop is uniquely distinct from existing course-based undergraduate research experiences (CUREs), whereby we adopt a crowd-science teaching approach in which students collaboratively build a genuine DL tool producing outputs that directly feed into an in-house AI platform under active development. In this Tips and Tools piece, we describe the pedagogical rationale, learning goals, session-wise structure, and assessment strategies for educators wishing to implement similar research-integrated activities. We argue that this crowd-science teaching model represents a transformative approach that can convert advanced AI antiviral drug design projects into scalable learning experiences to prepare the next generation of AI-fluent biomedical scientists. To our knowledge, our workshop is the first to situate students as direct contributors to a real DL platform for antiviral RNA therapeutic design.