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The way people learn has undergone a significant transformation thanks to the advancement of technology. Various digital tools complement daily and academic activities, facilitating access to updated and diverse information. The Internet, in particular, has positioned itself as the primary source of information, offering a large amount of textual and audiovisual content, with short content in the form of videos being the most popular. However, the learning process inevitably involves acquiring new concepts and terms. When encountering unfamiliar vocabulary in videos, people often search for additional information to understand the content better. Therefore, this research seeks to develop a tool capable of analyzing video transcripts using Natural Language Processing techniques to identify key terms and relate them to other relevant information sources, thus facilitating learning. When evaluating the relevance of terms to the textual content of videos on various topics using an Artificial Intelligence model, a relevance greater than 75% was evidenced for all terms. This confirms the efficacy of this approach for analyzing and understanding the textual content of transcribed videos.
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