AI-BASED EDUCATIONAL VIDEO SUMMARIZATION - Choudhury, Satish; Sharma, Mani Nandini; Sharma, Rajeev; Gandal, Ganesh Rambhau; Garg, Avni (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The proliferation of digital educational content in exponential amounts has led to the creation of an urgency among the efficient methods of summarization that can be used to create large instructional videos into meaningful and succinct features. Educational video summarization is an AI-powered system based on advanced machine learning and natural language processing and computer vision algorithms to provide short, context-rich summaries to make accessibility and understandability more accessible and consumer-friendly among learners. This method combines the multimodal analysis of data based on speech recognition, literature transcription, and understanding of the visual scene to determine the most important instructional points and eliminate superfluous information. Transformer based architectures of deep learning are used to learn semantic associations among spoken words, visual images, and instructional gestures. The models are used to extract relevant pedagogically coherent summaries in accordance with learning objectives. The suggested structure works in the steps of video segmentation, feature extraction, content ranking, and the creation of summaries. At the same time, visual attention models are used to examine the frame and identify slides, demonstrations, and the focus points of the instructor to make sure that the most important educational aspects are kept. The condensed version can be delivered as text-based, video-based, or a combination of both and it promotes adaptive learning systems and customized learning. The AI summarization has shown to be very effective in reducing cognitive overload, improved content discoverability and facilitated efficient learning as students can concentrate on the key information. In addition, it helps teachers and learning institutions in the production of highlight reels, course previews, and searchable knowledge bases. Consequently, this technology will provide a non-discriminatory learning environment in which different learners will enjoy personalized learning experiences. The future directions are to combine affective computing and learner-feedback to further streamline the summary relevance and pedagogical influence.