Real-Time Data-Driven Adaptive Mock Interview System Using LLMs and Explainable AI

A real-time, data-driven adaptive mock interview system that uses LLMs and explainable AI.

A real-time, data-driven adaptive mock interview system that uses LLMs and explainable AI. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are explanations of each result (feature importance or visual evidence), real-time processing of incoming data, text or speech data preparation, model training, fine-tuning or prompting, and evaluation with task-appropriate metrics. The reference build uses LLM APIs, SHAP, Apache Kafka, Python, and Hugging Face Transformers, with a clear project structure, setup instructions and sample data so that it can be run and extended.