Exploration of EduQuest – An AI-Powered System for Question Generation and Test Automation

Abstract

The increasing adoption of artificial intelligence in education has transformed traditional assessment practices by enabling automation, personalization, and efficiency in evaluating student learning. This paper presents EduQuest, an AI-driven framework designed for automated question generation and test management. The system leverages advanced natural language processing (NLP) models and large language models (LLMs) to generate high- quality, context-aware questions from diverse learning materials such as textbooks, lecture notes, syllabus and digital resources.

By incorporating multiple question formats such as multiple-choice, fill-in-the-blank, and short answer and also coding questions EduQuest supports adaptive testing aligned with curriculum objectives. Furthermore, the platform automates test assembly, delivery, grading, and feedback, reducing the workload on educators while ensuring fairness and scalability. A key feature of EduQuest is its integration of machine learning techniques for difficulty estimation, distractor generation, and performance prediction, enabling personalized assessments tailored to individual learner profiles.

The system not only enhances the reliability and objectivity of examinations but also fasters continuous learning by providing instant feedback and analytics to students and instructors. This paper reviews existing approaches, highlights the unique contributions of EduQuest, and outlines future opportunities for expanding AI-powered educational assessment tools to hybrid and online learning environments.