GENERATIVE AI BASED PERSONALIZED COURSE GENERATION PLATFORM
Hari krishnan, J and Arivazhagan, P. (2026) GENERATIVE AI BASED PERSONALIZED COURSE GENERATION PLATFORM. International Journal of Engineering Technology Research & Management (IJETRM). ISSN 2456-9348
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Abstract
The Generative AI Based Personalized Course Generation Platform is an intelligent educational system designed
to create customized learning paths for students based on their interests, skills, academic background, and learning
pace. Traditional online learning platforms often provide the same course structure for all users, which may not
effectively satisfy individual learning requirements. This project overcomes that limitation by using Generative
Artificial Intelligence techniques to automatically generate personalized course content, learning modules,
assessments, and recommendations for every learner. The platform analyzes user preferences, previous
performance, learning objectives, and subject interests using machine learning and natural language processing
techniques. Based on this analysis, the system dynamically generates tailored course materials, including notes,
quizzes, assignments, and topic suggestions. The platform can also recommend suitable learning resources such
as videos, articles, and practical exercises to improve student understanding and engagement. The proposed
system improves learning efficiency by adapting content difficulty and sequence according to the learner's
progress. It provides a flexible and interactive learning environment that supports self-paced education. In
addition, the platform includes features such as progress tracking, AI-based feedback, smart evaluation, and
performance analytics to help learners continuously improve their knowledge and skills.
| Item Type: | Article |
|---|---|
| Subjects: | Computer Applications > Artificial Intelligence |
| Domains: | Computer Science |
| Depositing User: | Mr IR Admin |
| Date Deposited: | 07 May 2026 18:01 |
| Last Modified: | 07 May 2026 18:01 |
| URI: | https://ir.vistas.ac.in/id/eprint/14069 |
