Automated Learning and Scheduling Assistant using LLM
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How to Cite

R., Mohanraj K, Abinayasankar M., and Balaji G B. 2024. “Automated Learning and Scheduling Assistant Using LLM”. Journal of Ubiquitous Computing and Communication Technologies 6 (3): 284-93. https://doi.org/10.36548/jucct.2024.3.006.

Keywords

— LLM
— RAG
— Prompt Engineering
— Education Process
— Automation
Published: 20-09-2024

Abstract

Large Language Models (LLMs) serve as the backbone of many AI applications, such as automatic content generation, virtual assistant and more. It is also used in automating the educational processes, such as scheduling the students’ assessments and managing teachers’ essential duties. The proposed study focuses on the design and development of an Automated Learning and Scheduling Assistant to facilitate the tasks like conducting unit test, managing internal assessment, and providing complete schedules of the students and the staff using LLM. The system is designed using the prompt engineering technique to improve the task automation efficiency. Retrieval-Augmented Generation (RAG) used helps in retrieving information and decision making, automating the test generation and the scheduling tasks. Data storage and retrieval are supported by the integration of the vector database. The primary objective of the proposed system is to enhance the educational process by automating the essential administrative and teaching functions, providing a scalable solution for the modern learning environment.

References

  1. Chen, Lijia, Pingping Chen, and Zhijian Lin. "Artificial intelligence in education: A review." IEEE Access 8 (2020): 75264-75278.
  2. Lalwani, Tarun, Shashank Bhalotia, Ashish Pal, Vasundhara Rathod, and Shreya Bisen. "Implementation of a Chatbot System using AI and NLP." International Journal of Innovative Research in Computer Science & Technology (IJIRCST) Volume-6, Issue-3 (2018).26-30.
  3. Karthick, S., R. John Victor, S. Manikandan, and Bhargavi Goswami. "Professional chat application based on natural language processing." In 2018 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), Bangalore, India IEEE, 2018. 1-4.
  4. Qin, Youming, Wei Xu, Adrian Lee, and Fu Zhang. "Gemini: A compact yet efficient bi-copter uav for indoor applications." IEEE Robotics and Automation Letters 5, no. 2 (2020): 3213-3220.
  5. Chen, Wei, Jincai Chen, Fuhao Zou, Yuan-Fang Li, Ping Lu, Qiang Wang, and Wei Zhao. "Vector and line quantization for billion-scale similarity search on GPUs." Future Generation Computer Systems 99 (2019): 295-307.
  6. Tapsai, Chalermpol. "Information processing and retrieval from CSV file by natural language." In 2018 IEEE 3rd International Conference on Communication and Information Systems (ICCIS), Singapore. IEEE, 2018. 212-216.
  7. Liang, Yuanyuan, Jianing Wang, Hanlun Zhu, Lei Wang, Weining Qian, and Yunshi Lan. "Prompting large language models with chain-of-thought for few-shot knowledge base question generation." arXiv preprint arXiv:2310.08395 (2023).
  8. Sonkar, Shashank, Andrew E. Waters, and Richard G. Baraniuk. "Attention word embedding." arXiv preprint arXiv:2006.00988 (2020).
  9. Madotto, Andrea, Zhaojiang Lin, Genta Indra Winata, and Pascale Fung. "Few-shot bot: Prompt-based learning for dialogue systems." arXiv preprint arXiv:2110.08118 (2021).
  10. Abbasian, Mahyar, Iman Azimi, Amir M. Rahmani, and Ramesh Jain. "Conversational health agents: A personalized llm-powered agent framework." arXiv preprint arXiv:2310.02374 (2023).
  11. Yan, Zhao, Nan Duan, Junwei Bao, Peng Chen, Ming Zhou, Zhoujun Li, and Jianshe Zhou. "Docchat: An information retrieval approach for chatbot engines using unstructured documents." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany. 2016. 516-525.
  12. Lewandowski, Tom, Emir Kučević, Stephan Leible, Mathis Poser, and Tilo Böhmann. "Enhancing conversational agents for successful operation: A multi-perspective evaluation approach for continuous improvement." Electronic Markets 33, no. 1 (2023): 39.
  13. Wiratunga, Nirmalie, Ramitha Abeyratne, Lasal Jayawardena, Kyle Martin, Stewart Massie, Ikechukwu Nkisi-Orji, Ruvan Weerasinghe, Anne Liret, and Bruno Fleisch. "CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering." In International Conference on Case-Based Reasoning, Cham: Springer Nature Switzerland, 2024. 445-460.
  14. Uc-Cetina, Victor, Nicolás Navarro-Guerrero, Anabel Martin-Gonzalez, Cornelius Weber, and Stefan Wermter. "Survey on reinforcement learning for language processing." Artificial Intelligence Review 56, no. 2 (2023): 1543-1575.
  15. Yang, Hao, Min Zhang, Daimeng Wei, and Jiaxin Guo. "Srag: speech retrieval augmented generation for spoken language understanding." In 2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT), Jilin, China. IEEE, 2024. 370-374.