Adaptive Testing Framework for AI Models (Psychometrics in AI Evaluation)

Adapting Questions to the student ability

This project considers the use of p-value weighting to voxel-based lesion behavior mapping (VLSM) studies. Our methods are demonstrated on an aphasia study investigating which regions of the brain are associated with the severity of language impairment among stroke survivors.

Item Response Theory (IRT) — the framework behind adaptive tests like GRE and PTE. Designed for AI engineers and data scientists to grasp core IRT concepts for adaptive testing systems.

1D Adaptive Testing Engine using Item Response Theory (IRT) | FastAPI · MongoDB Atlas · GPT-4o-mini | Dynamically adjusts GRE question difficulty based on real-time ability estimation

IRT-based adaptive testing engine with real-time question selection

An adaptive testing backend built with FastAPI and MongoDB that dynamically adjusts question difficulty based on student performance and generates personalized study recommendations.

Adaptive GRE prep tool using 1PL IRT with Fisher Information item selection for real-time ability estimation. LLM generates personalized study plans with curated resources. Built with FastAPI, MongoDB Atlas, Clerk Auth, and OpenRouter. 74 tests, Docker support, structured logging.

AI-powered adaptive testing engine using FastAPI, MongoDB, and IRT-based difficulty adjustment.

1D adaptive diagnostic engine, uses item response theory (Rasch Model) to dynamically select GRE - style questions based on student ability then generates a pesonalized AI study plan.