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Stream/Area:
-
Subject:
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Paper ID:
IJSREM62979
Volume:
10 |
5 |
May 2026
Pages:
1-18
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Artificial intelligence and machine learning
Sri Siddhartha Institute of Technology
Artificial intelligence and machine learning
Sri Siddhartha Institute of Technology

Artificial intelligence and machine learning
Sri Siddhartha Institute of Technology

Artificial intelligence and machine learning
Sri Siddhartha Institute of Technology

Artificial intelligence and machine learning
Sri Siddhartha Institute of Technology

Artificial intelligence and machine learning
Sri Siddhartha Institute of Technology
Traditional educational systems rely heavily on passive learning methodologies, where students manually capture information during lectures, often resulting in incomplete understanding and knowledge loss. With the rapid advancement of Artificial Intelligence, particularly Large Language Models (LLMs) and Speech Recognition systems, there is a significant opportunity to automate and enhance the learning process. This paper presents IntelLearn, an AI-driven smart learning platform designed to transform raw educational content—such as live lectures, YouTube videos, and uploaded multimedia—into structured learning resources including notes, quizzes, and presentation slides. The system integrates Deepgram & wispher v3 -based Automatic Speech Recognition (ASR) for accurate transcription and LLaMA 3.3 and 3.1-based Large Language Models for intelligent content generation. A key feature of the system is the implementation of a Retrieval-Augmented Generation (RAG) based AI tutor, which enables context-aware question answering by extracting relevant information from lecture content. Additionally, the platform supports multilingual output generation across English, Hindi, Kannada, and Telugu, improving accessibility for diverse learners. The system is built using a scalable full-stack architecture comprising FastAPI (backend), React (frontend), Supabase (database), and LiveKit (real-time communication). It incorporates caching mechanisms to optimise performance and reduce redundant AI processing. Experimental observations indicate that IntelLearn significantly reduces manual effort in note-taking and content preparation, improves learning efficiency, and enhances user engagement. The platform demonstrates the practical application of AI in modern education systems, offering a scalable and intelligent solution for automated learning.
KEYWORDS
This paper presented IntelLearn, an AI-driven smart learning platform that integrates speech recognition, natural language processing, and large language models to automate educational content generation and enhance learning experiences. The system effectively transforms raw inputs such as lectures, videos, and multimedia into structured outputs including notes, quizzes, and presentations. The integration of a Retrieval-Augmented Generation (RAG) based AI tutor enables context-aware responses, improving accuracy and reducing reliance on generic knowledge. Additionally, multilingual support enhances accessibility, making the system suitable for diverse learning environments. Experimental evaluation demonstrates that the system achieves reliable performance in transcription accuracy, content generation, and response relevance while maintaining acceptable latency. The platform reduces manual effort, improves learning efficiency, and provides an interactive educational experience. Overall, IntelLearn highlights the potential of Artificial Intelligence in transforming traditional education into a more intelligent, scalable, and accessible system. Future enhancements may include model optimisation, offline capabilities, and personalised adaptive learning features.
[1] T. Brown et al., “Language Models are Few-Shot Learners,” Advances in Neural Information Processing Systems (NeurIPS), vol. 33, 2020. [2] A. Radford et al., “Whisper: Robust Speech Recognition via Large-Scale Weak Supervision,” OpenAI, 2022. [3] P. Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” NeurIPS, 2020. [4] H. Touvron et al., “LLaMA: Open and Efficient Foundation Language Models,” Meta AI, 2023. [5] J. Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL, 2019. [6] S. Ji et al., “Evaluating Large Language Models for NLP Tasks,” IEEE, 2023. [7] M. Tavallaee et al., “A Detailed Analysis of the KDD Cup 99 Dataset,” IEEE Symposium on Computational Intelligence, 2009. [8] I. Sharafaldin et al., “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterisation,” ICISSP, 2018. [9] R. Vinayakumar et al., “Deep Learning Approach for Network Intrusion Detection,” IEEE Access, 2019. [10] H. Guo et al., “LogBERT: Log Anomaly Detection via BERT,” IEEE IJCNN, 2021.
Publication Date
5/9/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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