AI-Driven Intelligent Automated Task Allocation System Using Context Aware Decision Modeling And Dynamic Workload Management

References

[1] A. Vaswani et al., "Attention Is All You Need," in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 5998–6008, 2017.

[2] T. Brown et al., "Language Models are Few-Shot Learners," in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 1877–1901, 2020.

[3] A W. X. Zhao et al., "A Survey of Large Language Models," IEEE Transactions on Neural Networks and Learning Systems, vol. 14, no. 6, pp. 1–28, 2023.

[4] D. Ferraiolo, R. Sandhu, S. Gavrila, D. R. Kuhn, and R. Chandramouli, "Proposed Standard for Role-Based Access Control," ACM Transactions on Information and System Security, vol. 4, no. 3, pp. 224–274, 2001.

[5] R. S. Pressman and B. R. Maxim, Software Engineering: A Practitioner's Approach, 9th ed., McGraw-Hill, New York, 2020.

[6] P. Brucker, Scheduling Algorithms, 5th ed., Springer, Berlin, 2007.

[7] M. Wooldridge, An Introduction to MultiAgent Systems, 2nd ed., Wiley, Chichester, 2009.

[8] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Cambridge, MA, 2016.

[9] G. Adomavicius and A. Tuzhilin, "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.

[10] J. Smith and T. Brown, "Automated Task Management Systems Using Machine Learning Techniques," International Journal of Computer Applications, vol. 178, no. 10, pp. 1–7, 2020.

[11] A. Radford et al., "Language Models are Unsupervised Multitask Learners," OpenAI Technical Report, vol. 1, no. 8, pp. 1–24, 2019.

[12] D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed., Pearson, Upper Saddle River, NJ, 2021.

[13] C. C. Aggarwal, Artificial Intelligence: A Textbook, Springer, Cham, 2021.

[14] K. Schwaber and J. Sutherland, "The Scrum Guide: The Definitive Guide to Scrum — The Rules of the Game," Scrum.org, pp. 1–19, 2020.

[15] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient Estimation of Word Representations in Vector Space," in Proceedings of the International Conference on Learning Representations (ICLR), pp. 1–12, 2013.

@article{1715447,
      author = {Elumalai A, Dr. Ponmozhi K},
      title = {AI-Driven Intelligent Automated Task Allocation System Using Context Aware Decision Modeling And Dynamic Workload Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {2101-2108},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715447.pdf},
      abstract = {Manual task assignment in organizations is time-consuming, inconsistent, and leads to unbalanced workloads as team sizes grow. This paper presents an AI-driven intelligent task allocation system that automates the entire assignment process by integrating WhatsApp as the task submission interface with a Discord bot and Groq LLM. When a task message is received, the LLM evaluates the content, assigns a priority level, selects the most suitable worker from the Experts table in Supabase, and writes the structured record — containing task, priority, worker name, and worker email — directly into the Task table without any manual intervention. When all workers are occupied, the system identifies the worker nearest to completing their current task and assigns accordingly. A React frontend delivers role-specific dashboards, task tracking, pie chart reports, and inter-role chat for Super Admin, Admin, and Worker roles, while completed tasks are automatically archived into the CompletedTask table.},
      keywords = {Automated Task Allocation, Groq LLM, Discord Bot Integration, WhatsApp Task Pipeline, Dynamic Workload Balancing, Role-Based Access, Priority Classification, Expert Matching System.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715447}
  }