LECTRA: An AI-Based Lecture Assistant System Integrating Automatic Speech Recognition, Abstractive Summarisation, and Retrieval-Augmented Q&A

Abstract Lecture comprehension remains a critical bottleneck in higher education. Students retain less than 20% of auditory information after two weeks, and reviewing hours of raw audio is prohibitively time-consuming. This paper presents LECTRA, an AI-based Lecture Assistant System integrating four core NLP capabilities: Automatic Speech Recognition (ASR) using OpenAI Whisper, abstractive text summarisation using Facebook BART-Large-CNN, a Retrieval-Augmented Generation (RAG) question-and-answer module using RoBERTa, and automated quiz generation — all unified within a FastAPI web platform backed by SQLite. The system accepts audio, video, PDF, and plain-text inputs as well as YouTube URLs. Evaluation on 40 authentic academic lecture recordings (~20 hours) at Babcock University demonstrated a Word Error Rate (WER) of 9.8% on clean audio, ROUGE-1 of 0.4231, ROUGE-2 of 0.1987, ROUGE-L of 0.3814, and BLEU of 0.2156 for summarisation. A System Usability Scale (SUS) study with 12 undergraduate participants yielded 76.3/100 ('Good'). LECTRA handles varied regional accents without subscription requirements, offering a practical, low-cost tool that advances educational NLP. Keywords Natural Language Processing, Automatic Speech Recognition, Abstractive Summarisation, Retrieval-Augmented Generation, Educational Technology, Whisper, BART, RoBERTa, Higher Education