Automated Technical Documentation Generator from YouTube Video URL: A Multi-Module AI-Powered Web Application

• Author(s): Alex Franklin M; Ponmozhi K
• Paper ID: 1715675
• Page: 2747-2755
• Published Date: 30-03-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 9 March-2026
• DOI: https://doi.org/10.64388/IREV9I9-1715675

Abstract

The rapid proliferation of video-based learning content on platforms such as YouTube has created significant challenges for students, developers, and professionals who need to extract, retain, and reference technical knowledge from long-form instructional videos. Manual documentation is costly, existing summarization tools lose technical fidelity, and no current system addresses the full pipeline from a raw video URL to formatted, downloadable documentation. This paper presents an Automated Technical Documentation Generator, a nine-module AI-powered web application that accepts any YouTube URL and produces structured professional documentation in under 90 seconds. The system comprises modules for transcript extraction with automatic chunking, AI-based content type classification, LLM-driven chunk-and-merge documentation generation, a multi-turn Q&A chatbot, multi-format export in PDF, DOCX, and HTML, a usage history and analytics dashboard, a goal-driven Video Recommender, and a Cognitive Load Analyzer grounded in Sweller's Cognitive Load Theory. An automatic fallback router ensures near-100% availability across eight free LLM providers. Results on twenty test videos across five content types show transcript coverage exceeding 93% and strong alignment between Cognitive Load scores and independent educator assessments.

Keywords

Automated Documentation, Large Language Models, YouTube Transcript Processing, Cognitive Load Theory, Natural Language Processing, AI-Powered Education Tools, Knowledge Gap Detection, Video Content Analysis, Streamlit, LLM Fallback Routing.

Citations

IRE Journals:
Alex Franklin M, Ponmozhi K "Automated Technical Documentation Generator from YouTube Video URL: A Multi-Module AI-Powered Web Application" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 2747-2755 https://doi.org/10.64388/IREV9I9-1715675

IEEE:
Alex Franklin M, Ponmozhi K "Automated Technical Documentation Generator from YouTube Video URL: A Multi-Module AI-Powered Web Application" Iconic Research And Engineering Journals, vol. 9, no. 9, Mar. 2026, doi: https://doi.org/10.64388/IREV9I9-1715675

APA:
Alex Franklin M, Ponmozhi K (2026). Automated Technical Documentation Generator from YouTube Video URL: A Multi-Module AI-Powered Web Application. Iconic Research And Engineering Journals, 9(9). doi: https://doi.org/10.64388/IREV9I9-1715675

MLA:
Alex Franklin M, Ponmozhi K "Automated Technical Documentation Generator from YouTube Video URL: A Multi-Module AI-Powered Web Application" Iconic Research And Engineering Journals, vol. 9, no. 9, Mar. 2026. Crossref, https://doi.org/10.64388/IREV9I9-1715675

BibTeX

@article{1715675,
author = {Alex Franklin M, Ponmozhi K},
title = {Automated Technical Documentation Generator from YouTube Video URL: A Multi-Module AI-Powered Web Application},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {2747-2755},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715675.pdf},
abstract = {The rapid proliferation of video-based learning content on platforms such as YouTube has created significant challenges for students, developers, and professionals who need to extract, retain, and reference technical knowledge from long-form instructional videos. Manual documentation is costly, existing summarization tools lose technical fidelity, and no current system addresses the full pipeline from a raw video URL to formatted, downloadable documentation. This paper presents an Automated Technical Documentation Generator, a nine-module AI-powered web application that accepts any YouTube URL and produces structured professional documentation in under 90 seconds. The system comprises modules for transcript extraction with automatic chunking, AI-based content type classification, LLM-driven chunk-and-merge documentation generation, a multi-turn Q&A chatbot, multi-format export in PDF, DOCX, and HTML, a usage history and analytics dashboard, a goal-driven Video Recommender, and a Cognitive Load Analyzer grounded in Sweller's Cognitive Load Theory. An automatic fallback router ensures near-100% availability across eight free LLM providers. Results on twenty test videos across five content types show transcript coverage exceeding 93% and strong alignment between Cognitive Load scores and independent educator assessments.},
keywords = {Automated Documentation, Large Language Models, YouTube Transcript Processing, Cognitive Load Theory, Natural Language Processing, AI-Powered Education Tools, Knowledge Gap Detection, Video Content Analysis, Streamlit, LLM Fallback Routing.},
month = {March}
}