Design of online quiz application with AI feedback

Abstract

Online learning environments are increasingly prevalent, yet often struggle to provide timely, personalized, and diagnostic feedback, particularly for open-ended quiz responses. This limitation can hinder student learning and increase educator workload. This thesis addresses these challenges by designing, developing, and evaluating an "Online Quiz Application with AI Feedback." The system aims to leverage Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Machine Learning (ML) techniques, to analyze student answers and generate tailored feedback.

The research involves developing a web application with distinct interfaces for educators and students. A core component is a hybrid AI feedback engine combining rule-based logic for common errors with ML models (utilizing Python, TensorFlow/PyTorch, and Hugging Face Transformers) for nuanced analysis of open-ended textual responses and personalized recommendations for skill gap remediation.

This article outlines the theoretical foundations of AI in adaptive learning, details the system's architecture (FastAPI backend, React frontend), and describes the implementation of its core features. The methodology incorporates Agile development and User-Centric Design principles. The ultimate goal is to demonstrate the potential of AI to create more effective, efficient, and engaging learning experiences by enhancing the quality and immediacy of feedback in online quiz settings.