This paper proposes a novel artificial intelligence (AI)-powered system that leverages large language models (LLMs) to extract valuable insights from educational video content. The system processes video transcripts and user comments, utilizing sentiment analysis to identify and extract viewers’ questions. These questions are then cross-referenced with video transcripts to generate a comprehensive question-answering (QA) document. Unanswered viewer questions are flagged as potential topics for future content creation. The system was evaluated on a dataset of 1000 educational videos and 10,000 comments, achieving a 95% performance rate in identifying and responding to viewer questions. This approach enhances the educational content creation process by delivering direct answers to learner queries and informing future content development based on user interaction.