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

Abstract

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.

Introduction

The text presents StudyScribe, an AI-powered system designed to convert long educational lectures into concise, structured, and easy-to-understand study materials. With the increasing use of recorded lectures, online videos, and virtual classrooms, students often struggle to find important information in lengthy recordings. Manual note-taking can also lead to missed concepts and incomplete notes.

StudyScribe addresses these problems by combining Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Transformer-based models to automate lecture transcription, filtering, topic segmentation, and summarization.

Literature Review and Research Gaps

The study reviews ten research papers covering several AI and NLP technologies, including Transformers, BERT, BART, Sentence-BERT (SBERT), PEGASUS, T5, Longformer, automatic question generation, and Whisper. These models support speech recognition, semantic analysis, long-document processing, summarization, and quiz generation.

Three major research gaps are identified:

• Most existing systems perform individual tasks rather than integrating the entire lecture-processing workflow.

• Advanced AI models often require significant computational resources.

• Existing systems may include irrelevant conversations and announcements in the generated notes because they do not explicitly filter non-educational content.

StudyScribe aims to overcome these limitations through a unified pipeline specifically designed for educational lectures.

Proposed Methodology

The system follows five main processing stages:

1. Input Acquisition: Accepts audio files, video files, and YouTube links, converting them into a common audio format.

2. Speech-to-Text Conversion: Uses a Whisper-like model to convert lecture audio into a timestamped text transcript.

3. Transcript Cleaning and Relevance Filtering: Uses BERT-based semantic analysis to identify and remove announcements, casual conversations, and irrelevant discussions.

4. Topic Segmentation: Applies Sentence-BERT embeddings and cosine similarity to divide the transcript into meaningful, topic-wise sections.

5. Summary and Study-Aid Generation: Uses BART- or PEGASUS-like models to generate concise summaries, key points, important keywords, mind maps, and quiz questions.

The system also proposes a collaborative workspace where students can access, review, and improve shared study materials.

Expected Outcomes

StudyScribe is expected to reduce revision time, improve learning and knowledge retention, simplify catching up on missed lectures, and provide organized study resources for individual and group learning.

The system's performance will be evaluated using transcription accuracy, ROUGE-based summarization metrics, and topic-segmentation precision. However, the project has currently completed its requirements analysis and architectural design; implementation, integration, and performance evaluation are planned for the next phase.

Conclusion

This paper reviewed ten Transformer-based approaches to speech recognition, language representation and text summarization, and used the identified gaps — task isolation, high computational cost, and the absence of educational-relevance filtering — to motivate the design of StudyScribe, a proposed pipeline that combines automatic speech recognition, NLP-based content filtering, semantic topic segmentation, and abstractive summarization into a single system for converting raw lecture recordings into structured study material. Future work will focus on implementing each pipeline stage, integrating them into a working prototype, and evaluating the system on real lecture recordings against metrics such as transcription accuracy, summary quality (e.g., ROUGE scores), and topic-segmentation precision. Subsequent phases will also explore lighter-weight model variants to reduce the computational cost flagged as a limitation across the reviewed literature, so that StudyScribe remains practical to deploy for everyday student use.

References

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Copyright

Copyright © 2026 Mrs. Vanitha D, Shreya BM, Smitha R, Sunaina G Nayak, Sushma Raj R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.