Paper Contents
Abstract
This paper presents AI Video Assistant, an AI-driven, context-aware transcription and query system designed to facilitate knowledge-based discovery through intelligent video content analysis. The primary objective of this study is to evaluate the effectiveness of artificial intelligence in transforming passive video consumption into an interactive process of knowledge extraction, retrieval, and exploration. With the rapid growth of long-form video content in educational and professional domains, efficient access to relevant information has become a critical challenge, which this system aims to address. The proposed system integrates Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and semantic understanding techniques to generate accurate and searchable video transcriptions while preserving contextual meaning. By leveraging advanced language models, AI Video Assistant interprets user queries expressed in natural language and retrieves semantically relevant video segments with precise temporal alignment. This enables users to navigate directly to meaningful portions of video content without manual scanning, thereby improving information accessibility and learning efficiency. A comparative analysis with existing video analysis and transcription platforms demonstrates the system’s enhanced focus on contextual understanding, semantic query processing, and intelligent temporal navigation. Experimental results indicate improved knowledge retrieval accuracy, increased user engagement, and significant reductions in the time required to locate specific information within lengthy videos. Furthermore, the study addresses key challenges associated with AI-based video processing, including multilingual support, speaker diarization, technical vocabulary recognition, and privacy and ethical considerations related to automated content analysis.
Copyright
Copyright © 2026 Labeeb Raza Sayed, Mohd Ayyan Nayyum Siddiqui, Mohammad Muzammil Shaikh , Numan Nasir Shaikh, Rehan Momin. 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.
