International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 11 | Nov 2025 www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
INTELLIGENT QUESTION BANK WEB APPLICATION USING AI & ML Akshay Chandurkar, Aditya Gwalwanshi, Neha Gadge, Harshal Deshmukh Assistant Professor. Dr. Sushma Telrandhe Department of Computer Science and Engineering, Guru Nanak Institute of Engineering and Technology Nagpur 441501
-------------------------------------------------------------------***-------------------------------------------------------------Abstract—The rapid adoption of digital learning platforms has created a growing need for intelligent, accessible, and adaptive assessment tools in higher education. This paper presents the design and development of an Intelligent Question Bank Web Application tailored, leveraging Artificial Intelligence (AI) and Machine Learning (ML) to enhance academic engagement. The proposed system enables semantic search, automated question classification, difficultylevel prediction, and adaptive question recommendation, ensuring that students receive personalized learning experiences. Teachers can seamlessly manage question pools, generate balanced assessments, and analyze student performance through integrated analytics. The application architecture incorporates a scalable web-based frontend, a robust backend service layer, and AI-driven modules for semantic embeddings and recommendation. Initial results indicate improved efficiency in question retrieval and exam generation compared to traditional systems. This work demonstrates the potential of AI-powered academic tools to transform conventional examination practices into interactive, intelligent, and student-centric solutions. KEY WORDS: Index Terms—Adaptive Learning, Artificial Intelligence, Educational Technology, Intelligent Question Bank, Machine Learning, RTMNU, Semantic Search, Web Application
I. INTRODUCTION Education has undergone a massive transformation in the last decade, with digital learning platforms, online assessment tools, and artificial intelligence (AI)–driven systems revolutionizing the way students learn and teachers evaluate performance. Traditional methods of question paper setting and preparation for examinations often involve repetitive manual tasks, limited question diversity, and lack of adaptive assessment strategies. In the context of higher education institutions such as Rashtrasant Tukadoji Maharaj Nagpur University (RTMNU), the first-semester curriculum covers a wide range of foundational subjects that are essential for academic progression. However, the process of preparing and maintaining a comprehensive question bank for these courses remains a challenging and time-consuming task. This research focuses on the development of an Intelligent Question Bank Web Application that leverages Artificial Intelligence (AI) and Machine Learning (ML) to © 2025, IRJET
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Impact Factor value: 8.315
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automate and enhance the management of academic question repositories. The conventional question bank systems used in many universities primarily rely on static storage mechanisms, where questions are manually categorized by instructors based on topics, difficulty levels, or past examination trends. While this approach ensures control, it lacks flexibility, scalability, and adaptability to evolving academic needs. Furthermore, students often face difficulty accessing a well-structured and diversified set of questions for effective self-assessment and exam preparation. As education moves toward a more datadriven and personalized learning approach, there is a pressing need for an intelligent system that can dynamically generate, categorize, and analyze questions according to student performance, syllabus coverage, and cognitive learning levels. Recent developments in Natural Language Processing (NLP) and Machine Learning have provided new opportunities for enhancing educational tools. AI-powered systems can now automatically analyze text-based content, extract relevant questions, predict question difficulty, and even recommend practice sets based on user learning patterns. Such systems have demonstrated the potential to not only reduce the workload of educators but also improve student engagement and academic outcomes. However, despite these technological advancements, the integration of AI-driven solutions within Indian universities, particularly under RTMNU, remains limited. This gap presents a unique opportunity to introduce a web-based platform specifically designed for the RTMNU First Semester syllabus that intelligently manages and generates examination questions. The proposed Intelligent Question Bank Web Application aims to bridge this gap by developing a smart, user-friendly platform that allows both instructors and students to interact with an AI-driven system for question generation and selection. The system utilizes machine learning algorithms to analyze question patterns, classify questions based on Bloom’s Taxonomy levels, and recommend question sets for different difficulty levels or specific topics. Additionally, the web application will feature real-time analytics to track
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