AI-Driven Employee Support Systems: Enhancing Workplace Assistance through Chatbots and Large Language Models (LLMs)

Author

Arshdeep Singh, Veerpal, Dr Manju Rani

Abstract

 AI-driven employee support systems use chatbots and large language models (LLMs) to move workplace assistance beyond static, rule-based tools. This paper proposes the Brahma AI Support System Theory, a conceptual framework built on three layers - cognitive, adaptive, and contextual - that let a support system understand natural-language queries, learn from past interactions, and respond in a way that fits the employee's role and situation. Combined with a single interface across enterprise applications, the framework aims to reduce system fragmentation, speed up access to information, and improve employee experience and productivity. The paper reviews prior work on AI-based support systems and LLM-driven chatbots, outlines the structure and working of the proposed system, and discusses how existing research on personalization, human-AI collaboration, and responsible AI supports the theory, along with the governance and privacy challenges organizations must manage when adopting it.

Keywords

Artificial Intelligence, Employee Support Systems, Chatbots, Large Language Models (LLM), Conversational AI, Natural Language Processing, Intelligent Workplace Systems, AI Automation

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References

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