CLARITAS: Digitizing Educational Feedback Collection Through AI-Driven Sentiment Analysis and Agentic Orchestration

Traditional feedback collection in educational institutions is often paper-based, resulting in delayed analysis and limited utilization of student insights. This paper presents CLARITAS, an AI-powered educational feedback analytics platform designed to digitize and automate feedback collection and analysis for classrooms and training sessions. Featuring a role-based architecture for administrators, teachers, and students, the system analyses feedback using locally deployed DistilBERT for sentiment classification, Term Frequency-Inverse Document Frequency (TF-IDF) for keyword extraction, and word-frequency analysis. A self-built, restricted Agentic AI layer coordinates these components to generate structured summaries without relying on external LLMs or third-party APIs. A fully local analytical workflow supports data privacy, institutional governance, and traceability. The proposed approach provides a privacy-conscious and scalable alternative to conventional manual feedback analysis.