Built an adaptive quiz generator using Groq’s LLaMA-4-Scout — looking for feedback on difficulty estimation + user modeling

Hi all — I’m a UC San Diego undergrad working on a project that combines LLMs with adaptive learning theory. It’s called AscendQuiz, and the idea is simple: upload any educational PDF (lecture notes, textbook chapters, etc.), and the app builds a personalized, mastery-based quiz using a large language model.

Behind the scenes:

I’m using Groq’s LLaMA-4-Scout-17B-16E-Instruct for question generation Each question is labeled with a predicted correctness percentage (e.g., 72% of students would likely answer this correctly) A lightweight adaptive quiz engine routes students to harder/easier questions in real time Mastery is defined as answering 5+ “hard” questions (difficulty tiers 6–8) at ≥75% accuracy Real-time feedback and explanations are generated after each response

My goals:

Prototype a lightweight, curriculum-agnostic adaptive testing system Experiment with how well a generative model can approximate IRT-style difficulty using predicted correctness Get feedback from students and from the ML community on modeling assumptions and future improvements

If you’d like to test it or explore the model behavior:

Try it: https://ascend-quiz.streamlit.app
Feedback form: https://forms.gle/WW9x9cAyudjJjRB78
GitHub: https://github.com/a7arora/computer-adaptive-mastery-quiz

Would love input on:

Validity of the difficulty estimation approach (predicted correctness as a proxy) Suggestions for improving adaptation logic or fallback strategy Any thoughts on making it more robust for general content domains

Thanks!