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
AI-Driven Automated Course Generation: A Next-Generation Framework for Scalable Digital Learning Chaitra K G1, Ramya Chaithra N S2, Sangeetha Sarji3, Tanmaye P Bisleri4, Vrunda N C5 1Assistant Professor, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Davangere, affiliated to VTU Belagavi, Karnataka, India.
2,3,4,5Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and
Technology, Davangere, affiliated to VTU Belagavi, Karnataka, India. -----------------------------------------------------------------------------***-----------------------------------------------------------------------------Abstract—The rapid growth of digital learning has INTRODUCTION increased the demand for high-quality educational content that is scalable, adaptive, and accessible. However, creating structured courses manually remains a time-consuming and expertise-driven process, limiting the capacity of educators, institutions, and content creators to produce diverse and comprehensive learning materials. This research proposes an AI-Driven Automated Course Generation System designed to address these challenges by integrating generative artificial intelligence with modern web technologies. The system leverages Google’s Gemini generative models to autonomously generate complete course structures—including titles, descriptions, chapters, and detailed content—based on user-provided inputs such as topic, category, and difficulty level.
The rapid expansion of digital learning platforms, online education systems, and self-paced training programs has significantly transformed the global education landscape. With millions of learners shifting toward online modes of instruction, the demand for structured, high-quality, and up-to-date educational content has increased exponentially. Despite this growth, the creation of complete and pedagogically sound courses remains a complex, timeintensive, and expertise-driven task. Educators, content creators, and institutions often spend extensive time developing course outlines, writing chapter material, designing assessments, and preparing complementary multimedia resources. This manual process limits scalability, slows curriculum development, and creates inconsistencies in learning experiences.
Built using Next.js 15, React 19, and Tailwind CSS on the frontend, and powered by Neon Serverless PostgreSQL with Drizzle ORM on the backend, the framework ensures seamless data handling, improved responsiveness, and scalable performance. Authentication is managed through Clerk, enabling secure and personalized user access. The system employs a modular architecture consisting of course-creation APIs, chapter-generation pipelines, and a dynamic dashboard for course management. Experimental evaluation demonstrates that the proposed solution reduces manual course creation time by over 80%, enhances consistency in content structure, and significantly increases the volume and diversity of educational material generated within a given timeframe.
Recent advancements in Generative Artificial Intelligence (GenAI) present new opportunities to automate and enhance educational content production. Large Language Models (LLMs) such as Google Gemini, GPT-based systems, and other transformer-based architectures have shown remarkable capability in understanding context, generating human-like text, and synthesizing structured information. These technologies are increasingly used in learning analytics, automated feedback systems, adaptive tutoring, and intelligent content recommendation. However, very few systems provide end-to-end automation of course generation—covering course design, chapter creation, and structured content development—within a unified and deployable architecture.
This research highlights the transformative potential of generative AI in automating curriculum development and demonstrates a practical, deployable framework capable of supporting educators, training organizations, and elearning platforms. The findings suggest that AI-assisted course generation can serve as a foundation for future advancements such as personalized learning paths, multilingual course creation, and automated video-based instructional generation. The proposed system represents a step forward in scalable e-learning automation and contributes to the broader field of AI-enhanced education technology.
© 2025, IRJET
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Impact Factor value: 8.315
This research introduces an AI-Driven Automated Course Generation System, a modern web-based platform that utilizes generative AI models to autonomously create complete online courses based on user-defined topics, categories, and difficulty levels. The system integrates Google’s Gemini models for generating course outlines, detailed chapter explanations, and supplementary material. It is developed using Next.js 15 for the frontend, React 19 for component management, Tailwind CSS for responsive design, and Neon Serverless PostgreSQL with Drizzle ORM for robust backend data handling. User authentication and
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