AI-driven context-aware trimming and segmentation of educational video content for focused learning

Abstract

Recorded class lectures and tutorials are essential learning resources, but unedited raw videos generally contain significant non-instructional content, such as silence, administrative discussions, off-topic instructions, making effective navigation and revision difficult. This study presents a framework which is open-source, contextaware and can automatically detect and remove irrelevant or off-topic segments from long, unedited educational videos. Most of the existing related solutions rely heavily on large, closed-source models or fixed-length video segmentation with no context awareness, which are computationally expensive and prone to breaking semantic continuity. To address these limitations, we propose a modular, four-stage orchestration pipeline designed to run entirely on consumer-grade GPUs using small, open-weight models. The framework uses automatic speech recognition (Parakeet TDT 0.6B) to perform dynamic, sentence-level video segmentation, ensuring that each segment represents a complete semantic unit. A lightweight vision language model then generates structured textual descriptions for each segment using both visual frames and subtitles, augmented with rolling local context to preserve chronological coherence. Finally, a small language model (Qwen3-4B-instruct) is used to perform classification on the generated descriptions rather than on raw video input. The proposed orchestrator pipeline achieves 96.85% accuracy, 89.04% precision, 92.77% recall, and 90.86% F1 for irrelevant-segment detection on a human-annotated benchmark of 20 educational videos (total duration 09:13:47). This results in a significant improvement in content preservation over a one-shot Gemini-3 baseline, with precision increasing from 45.98% to 89.04% and accuracy from 81.63% to 96.85%. The entire system runs locally via quantized inference, offering a practical and privacypreserving alternative to cloud-based solutions.