A retrieval-augmented AI platform for adaptive oncology education grounded in clinical practice guidelines
Each module implements specific learning science principles through concrete architectural decisions, sharing a common RAG knowledge grounding layer.
Generates personalized oncology chapters on demand, adapted to Medical Student, Resident, or Attending expertise levels. Content is grounded in NCCN guidelines and foundational textbooks via RAG, with embedded retrieval practice checkpoints.
Dr. Chandra, an AI attending oncologist, uses Socratic questioning and progressive disclosure to guide clinical reasoning. Enforces NCCN compliance and introduces "desirable friction" to prevent automation bias.
Generates high-yield flashcards (MOA, TNM cutoffs, pathognomonic findings) from guideline and textbook context. SM-2 algorithm schedules reviews for optimal long-term retention.
Every architectural decision maps to an established learning science framework.
All generated content is grounded in authoritative clinical sources via Gemini File Search RAG, with automated citation extraction from grounding metadata.
Full preprint describing the system design, architecture, pedagogical rationale, and technical validation.
@article{ashish2026oncoshikshak,
title={Onco-Shikshak: A Retrieval-Augmented AI Platform
for Adaptive Oncology Education Grounded in
Clinical Practice Guidelines},
author={Ashish},
journal={arXiv preprint},
year={2026},
url={https://inventcures.github.io/onco-shikshak/}
}