An AI-Powered Adaptive Learning and Interview Preparation Platform Using Natural Language Processing and Machine Learning

The increasing competitiveness of academic examinations and technical recruitment processes has intensified the need for learning systems that can adapt to individual user performance. Conventional platforms for learning and interview preparation are largely based on static content and manual evaluation, which limits their ability to provide consistent feedback and assess conceptual understanding in a meaningful way. The proposed system integrates Natural Language Processing (NLP) and Machine Learning (ML) techniques to support subject-based learning and interview preparation within a unified framework. The platform enables multi-round interview simulation and allows users to practice using uploaded study materials. Textual responses are processed using TF-IDF and cosine similarity to evaluate descriptive answers, while machine learning models utilize performance-related features to estimate user skill levels and identify learning patterns. The system is implemented using a full-stack architecture consisting of a React-based frontend, a Flask-based backend, and a MySQL database. Observations from system evaluation indicate that the approach can distinguish variations in answer relevance and provide structured feedback to users. The design supports scalable deployment and can be applied in both academic and training-oriented environments.