Review on AI- Driven Online Quiz Assessment System

Abstract

This system introduces a smart quiz application that uses AI to create questions based on topics chosen by users. Traditional quiz systems depend on fixed question banks, which often lead to outdated content, limited choices, and repetitive assessments. In contrast, this application uses the Open Router API, a doorway to language models, to build a flexible and scalable way to generate questions. Users can input a topic of interest, and the backend system interacts with the Open Router API to get fresh, relevant, and context-sensitive questions. The system architecture combines ReactJS for the frontend and Node.js for the backend, along with Open Router for AI integration. This improves user engagement and provides real-time, personalized learning experiences. The results show high accuracy, relevance, and educational value in the AI-generated content. This method plays a significant role in adaptive learning technologies and highlights the potential of generative AI in modern educational tools.

Introduction

The rapid growth of digital education has highlighted the limitations of traditional quiz systems, which rely on fixed, manually created question banks and lack adaptability and personalization. These static approaches often lead to repetitive content, reduced engagement, and limited alignment with a learner’s progress. To address these issues, this project proposes an AI-powered dynamic quiz application that generates quiz questions in real time using generative AI models accessed through the Open Router API.

The system allows learners to select topics, after which relevant and varied quiz questions are automatically created on demand. By eliminating dependence on predefined datasets, the platform improves content diversity, relevance, and learner engagement. Built using ReactJS for the frontend and Node.js for the backend, the application provides a scalable, responsive, and interactive user experience while demonstrating the potential of conversational and generative AI in education.

The literature review highlights advancements in AI-driven question generation, NLP-based content validation, and adaptive assessment systems. Prior research shows that transformer-based models can effectively generate high-quality multiple-choice questions, while modern web frameworks support scalable and user-friendly educational platforms. However, many existing systems still lack real-time adaptability and deep personalization.

The methodology introduces a knowledge-driven approach using Domain Knowledge and an innovative Quiz Ontology, enabling dynamic quiz creation, precise knowledge gap identification, and personalized assessments. Quiz questions and answer options are generated and linked to specific learning concepts at runtime, allowing flexible and learner-specific evaluation.

Results show that the system provides detailed feedback, performance tracking, and personalized improvement suggestions after each quiz. Overall, the proposed platform transforms traditional quizzes into an intelligent, adaptive assessment tool that enhances engagement, learning retention, and accessibility in modern digital education.

Conclusion

The development of the AI-powered quiz generation platform marks a significant step in modern education and personalized learning experiences. By integrating smart prompt engineering, real-time AI interaction, and smooth frontend-backend communication, the system changes how quizzes are created, delivered, and experienced. Using technologies like ReactJS, Node.js, and the Open Router API, the platform ensures dynamic content generation, adapts to user preferences, and offers an engaging user interface. It provides an easy way for learners to test their knowledge and gives educators powerful tools for content customization and performance tracking. As the system grows, it sets the stage for advanced features like adaptive difficulty levels, voice-enabled question answering, and support for multiple languages—leading to the next generation of AI-assisted educational platforms that are inclusive, scalable, and highly personalized.

References

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Copyright

Copyright © 2026 Prof. Surbhi R Khare, Miss. Dhanashri Lohakare, Miss. Rujuta Shende , Miss. Vaishnavi Bande, Mr. Ashwin Dhoke, Mr. Sahil Dhumne. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.