EduConvo: AI-Driven Student Feedback System

Abstract

Student feedback is a crucial tool for educational institutions to assess teaching effectiveness and improve course delivery. However, traditional feedback collection methods, e.g., static surveys, suffer from low engagement, vague responses, and a lack of actionable insights. To address these limitations, this paper presents a Conversational AI-based Student Feedback System that uses a Large Language Model (LLM) to facilitate dynamic, interactive, and adaptive feedback collection. The system personalizes questions based on course content, allowing in-depth responses while maintaining anonymity.

 The system uses Next.js for the frontend, Flask for the backend, and a MongoDB database for data storage, integrating OpenAI’s GPT model for conversational interactions. A real-time analytics dashboard enables faculty to interpret feedback effectively. To evaluate the system, a comparative study was conducted against a survey-based feedback approach, measuring student engagement, response quality, and usability of the system. The results indicate a significant improvement in feedback depth, participation rates, and user satisfaction.

 This research highlights the role of AI-driven feedback systems in enhancing student engagement and providing richer insights for academic institutions.