StudyScribe: An AI-Powered Audio Processing and Smart Learning System for Automated Lecture Note Generation

Digital learning increasingly relies on recorded lectures, but these recordings are long, unstructured, and mixed with content that has no academic value, which makes revision slow and error-prone. This paper presents StudyScribe, a proposed AI-powered pipeline that accepts a lecture as an audio file, video file, or YouTube link and converts it into structured, topic-wise study material. The system combines Transformer-based automatic speech recognition, NLP-based relevance filtering, semantic topic segmentation, and abstractive summarization to remove non-educational content and generate concise notes, key insights, and quiz questions. A review of ten related works spanning speech recognition and text summarization is used to justify the design choices, and the resulting five-stage architecture — input acquisition, transcription, cleaning, segmentation, and summary generation — is described along with directions for implementation and evaluation.