Intelligent Assistant for Real-Time Classroom Updates Using NLP

Abstract

Educational technology development at a fast pace has resulted in the creation of intelligent systems which improve classroom communication along with management processes. The research creates an Intelligent Assistance framework which uses NLP along with ANN for real-time classroom notification automation and important academic message delivery. The system applies powerful NLP methods to handle unstructured text data so it can pull valuable information from assignments alongside exam schedules and announcements. The model uses ANN to discover sequential word patterns in classroom dialogue which supports its ability to create accurate tailored updates. The established system addresses Learning Management Systems (LMS) restrictions through automated information recuperation procedures with updated notification delivery functionalities. The system gets trained using various educational data sources to achieve high scalability together with robustness and precision. Testing indicates the intelligent system boosts student-faculty classroom participation and diminishes response time and increases accessibility of education data.

Introduction

Manual announcements, basic LMS platforms, and emails are inefficient for educational organizations due to slow communication, limited automation, and poor classroom engagement. To address these issues, AI-powered virtual assistants using Natural Language Processing (NLP) and Artificial Neural Networks (ANN) offer smarter automation for real-time classroom updates. These systems extract and analyze academic information—like assignment deadlines, exam schedules, and faculty announcements—and deliver personalized notifications to students and educators, improving communication and learning connectivity.

Existing AI educational tools and chatbots often struggle with context understanding, real-time updates, and personalized messaging. This research proposes an intelligent assistant system that automates information extraction from unstructured educational data, classifies queries using ANN, and sends tailored notifications using advanced NLP methods.

The system architecture includes modules for data collection (from LMS and university portals), preprocessing (text cleaning, tokenization, lemmatization), query classification via ANN, and real-time personalized notifications through web, mobile, email, and chatbot interfaces. The backend uses Flask and the frontend employs React.js, hosted on AWS Lambda for scalability.

Evaluations on 10,000+ queries show 92% accuracy in classification, fast response times, and positive user feedback. The system also incorporates robust security measures such as role-based access control and data encryption to comply with privacy regulations.

Conclusion

Real-time classroom communication becomes enhanced through NLP and ANN technology which enables automated academic update extraction and classification followed by delivery through the Intelligent Assistant for Real-Time Classroom Updates. The system improves upon Learning Management Systems (LMS) by establishing real-time alerts as well as tailored notifications alongside its intelligent query response system. The proposed model reached query classification accuracy of 91.9% while providing information in 355 milliseconds through experimental tests which reinforced efficient information delivery. The comparative assessment showed Google Classroom alongside Microsoft Teams have no automated systems for query classifying and context understanding. Education enhancement occurs through the proposed system which unites AI information retrieval methods with NLP context awareness capabilities. User satisfaction surveys showed a remarkable success rate of 92.0% which confirms the system meets practical educational standards. The system exhibits outstanding performance yet faces three main challenges regarding unclear query interpretation and difficulties in processing large data sets as well as multilingual capabilities. Transformer models (particularly BERT and GPT) will be integrated with the system for semantic enhancement while adding voice capabilities and reinforcement learning will improve adaptable responses. An expansion of LMS integration coupled with cloud-based deployment will improve system accessibility together with its scalability characteristics. Modern education benefits through artificial intelligence automation because it delivers an expandable intelligent interactive program which strengthens classroom operations for both students and instructors. Such enhancements will enable the system to transform digital education through the development of automated learning spaces that are accessible to students.

References

[1] S. A., M. V., R. V., and U. B., \"COLLEGEBOT: Virtual Assistant System for Enquiry Using Natural Language Processing,\" 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2024, pp. 1407-1414, doi: 10.1109/IDCIoT59759.2024.10467608. [2] R. Josphineleela, S. Kaliappan, N. L., and U. M. Bhatt, \"Intelligent Virtual Laboratory Development and Implementation using the RASA Framework,\" 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2023, pp. 1172-1176, doi: 10.1109/ICCMC56507.2023.10083701. [3] R. Punugoti, R. Duggar, R. R. Dhargalkar, and N. Bhati, \"Intelligent Healthcare: Using NLP and ML to Power Chatbots for Improved Assistance,\" 2023 International Conference on IoT, Communication and Automation Technology (ICICAT), Gorakhpur, India, 2023, pp. 1-6, doi: 10.1109/ICICAT57735.2023.10263708. [4] J. Kang and W. Yang, \"Research on Teaching Strategies to Promote Classroom Interaction in Smart Classroom Environment,\" 2023 International Symposium on Educational Technology (ISET), Ho Man Tin, Hong Kong, 2023, pp. 121-123, doi: 10.1109/ISET58841.2023.00032. [5] F. Bouslama and F. Kalota, \"Creating smart classrooms to benefit from innovative technologies and learning space design,\" 2013 International Conference on Current Trends in Information Technology (CTIT), Dubai, United Arab Emirates, 2013, pp. 102-106, doi: 10.1109/CTIT.2013.6749486. [6] S. Wang and Z. Xin, \"Research on Digital Classroom Construction in the Information Age under Computer Big Data Technology,\" 2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE), Ottawa, ON, Canada, 2022, pp. 245-249, doi: 10.1109/CIPAE55637.2022.00059. [7] C. S. Pillai, R. Pedram, N. Salumuri, D. Nath, and K. Pakhrani, \"Park-Shield: Two-Wheeler Anti-Theft System,\" 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India, 2022, pp. 1-5, doi: 10.1109/ICCUBEA54992.2022.10010876. [8] OpenAI, \"GPT-based Educational NLP Models,\" AI Research Journal, vol. 8, no. 2, pp. 45-57, 2023. [9] T. Mikolov, K. Chen, G. Corrado, and J. Dean, \"Efficient Estimation of Word Representations in Vector Space,\" Proceedings of the International Conference on Learning Representations (ICLR), Scottsdale, Arizona, USA, 2013. [10] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, ?. Kaiser, and I. Polosukhin, \"Attention is All You Need,\" Advances in Neural Information Processing Systems (NeurIPS), 2017. [11] Y. Goldberg and O. Levy, \"Word2Vec Explained: Deriving Mikolov et al.\'s Negative-Sampling Word-Embedding Method,\" arXiv preprint arXiv:1402.3722, 2014. [12] J. Devlin, M. Chang, K. Lee, and K. Toutanova, \"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,\" Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Minneapolis, MN, USA, 2019, pp. 4171-4186. [13] T. Young, D. Hazarika, S. Poria, and E. Cambria, \"Recent Trends in Deep Learning Based Natural Language Processing,\" IEEE Computational Intelligence Magazine, vol. 13, no. 3, pp. 55-75, 2018. [14] A. Radford, J. Wu, R. Child, D. Luan, and I. Sutskever, \"Language Models are Few-Shot Learners,\" Advances in Neural Information Processing Systems (NeurIPS), 2020. [15] M. Ruder, \"An Overview of Multi-Task Learning in Deep Neural Networks,\" arXiv preprint arXiv:1706.05098, 2017.

Copyright

Copyright © 2025 Dr. V. Raviteja, Ch. Chalani Priyanka, K. Abhiram Sai , N. Sai Samhitha, V. Pavani. 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.