Authors : E. Hanuman Sai Gupta; P. V. S. R. Chandramukhi; N. S. K. Rangayya; Y. Lahari; K. S. Divya; N. N. Venkata Sai; Ch. Dasarath

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/342np5u6

Scribd : https://tinyurl.com/yk7ct5r2

DOI : https://doi.org/10.38124/ijisrt/25nov1577

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In Today's time, the students would be wasting their time darting between different places searching for quality learning content regarding a vast array of subjects. The today's project eliminates all of this by constructing a full, customized mini-course—with individually chosen videos and interactive quizzes—on any subject, all within one easy platform. This accelerates learning, aggregates it, and much more engaging. The project suggests an AI-based Course Builder web application in which a person can create customized learning sequences on any subject of interest. Unlike the conventional learning platforms based on pre-designed course blueprints or rigid schedules, the system creates three-lesson mini-courses automatically based on an input of a user like subject and grade. Video Integration: The GPT creates a proper and pertinent title for the lesson, which is transmitted to the YouTube Data Application Programming Interface (API) to retrieve a good quality video to link with the learning objective of the provided lesson. Quiz Generation- Based on the sub- topic and title of the lesson, and contextual content, an interactive short quiz is created with the help of the GPT model again. The quizzes promote learning and give immediate feedback. The course is designed as an interactive, scrollable site with inbuilt videos and quizzes whereby students can study at their convenience. There is no calendar management or scheduling involved, which simplifies the process while providing flexibility.

Keywords : Generative AI, Personalized Learning, Educational Technology, Automated Quiz Generation, LLM, YouTube API.

References :

1. I. Gligorea, M. Cioca, R. Oancea, A.–T. Gorski, H. Gorski, and P. Tudorache, “Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review,” Education Sciences, vol. 13, no. 12, article 1216, Dec. 2023. Available: https://www.mdpi.com/2227-7102/13/12/1216
2. M. J. Mustfa and S. Ashiq, “Harnessing the Power of Artificial Intelligence for Adaptive Learning Systems: A Systematic Review,” International Journal of Education & Management Engineering, vol. 14, no. 5, pp. 12–22, 2024.
3. R.-S. Lu, C.-C. Lin, and H.-Y. Tsao, “Empowering Large Language Models to Leverage Domain-Specific Knowledge in E-Learning,” Applied Sciences, vol. 14, no. 12, article 5264, June 2024. Available: https://www.mdpi.com/2076-3417/14/12/5264
4. M. R. Kabir and F. Lin, “An LLM-Powered Adaptive Practicing System,” in Proc. CEUR-WS, vol. 3487, 2023. Available: https://ceur-ws.org/Vol-3487/paper1.pdf
5. C. Dong, Y. Yuan, K. Chen, S. Cheng, and C. Wen, “How to Build an Adaptive AI Tutor for Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG),” arXiv preprint arXiv:2311.17696, Nov. 2023. Available: https://arxiv.org/abs/2311.17696
6. A. Celik and Q. H. Mahmoud, “A Review of Large Language Models for Automated Test Case Generation,” Machine Learning and Knowledge Extraction, vol. 7, no. 3, p. 97, 2025. Available: https://www.mdpi.com/2504-4990/7/3/97
7. S. Bulathwela, H. Muse, and E. Yilmaz, “Scalable Educational Question Generation with Pre-trained Language Models,” arXiv preprint, May 2023. Available: https://arxiv.org/abs/2305.07871
8. R. Gao, H. E. Merzdorf, S. Anwar, M. C. Hipwell, and A. Srinivasa, “Automatic Assessment of Text-Based Responses in Post-Secondary Education: A Systematic Review,” arXiv preprint, Aug. 2023. Available: https://arxiv.org/abs/2308.16151
9. S. Gevorgyan, “The Use of Adaptive Learning Technologies in e-Learning for Inclusive Education: A Systematic Review,” E-Learning Innovations Journal, vol. 2, no. 1, pp. 90–107, Mar. 2024. Available: https://www.el-journal.org/index.php/journal/article/view/16
10. P. K. Kang, “AI in Education: Personalized Learning Through Adaptive Systems,” International Journal of Machine Learning & Artificial Intelligence, vol. 4, no. 4, 2023. Available: https://jmlai.in/index.php/ijmlai/article/view/51
11. D. Chiotaki, V. Poulopoulos, and K. Karpouzis, “Adaptive Game-Based Learning in Education: A Systematic Review,” Frontiers in Computer Science, vol. 5, 2023. Available: https://www.frontiersin.org/articles/10.3389/fcomp.2023.1062350/full
12. I. Papachristou, G. Dimitroulakos, and C. Vassilakis, “Automated Test Generation and Marking Using LLMs,” Electronics, vol. 14, no. 14, article 2835, 2025. Available: https://www.mdpi.com/2079-9292/14/14/2835
13. “Large Language Models for Generating Medical Examinations: Systematic Review,” BMC Medical Education, vol. 24, article 354, 2024. Available: https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-024-05239-y
14. “Quality Assurance and Validity of AI-Generated Single Best Answer Questions,” BMC Medical Education, 2025. Available: https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-025-06881-w
15. “TwinStar: A Novel Design for Enhanced Test Question Generation Using Dual-LLM Engine,” Applied Sciences, vol. 15, no. 6, article 3055, 2025. Available: https://www.mdpi.com/2076-3417/15/6/3055