I n F i C on D: Investigating Interactive No‐code Fine‐tuning with Concept‐based Knowledge Distillation

Knowledge distillation is a widely used technique whereby knowledge from large pre‐trained models is transferred into smaller student models. However, this process is non‐trivial and traditionally requires technical and theoretical expertise in AI/ML. We investigate a visualization‐driven strategy for making this process more accessible and intuitive via developing I n F i C on D, a novel tool that leverages visual concepts to scaffold the knowledge distillation process and support subsequent no‐code fine‐tuning of student models. I n F i C on D's backend pipeline extracts text‐aligned visual concepts and constructs highly interpretable student models; its frontend supports interactively fine‐tuning these student models by directly manipulating concept influences. Empirical evaluations help validate that I n F i C on D effectively supports knowledge distillation and subsequent fine‐tuning workflows. We additionally discuss insights and lessons learned about how human‐in‐the‐loop and visualization‐driven approaches like I n F i C on D can support accessible and adaptable AI explainability and model distillation.