The integration of Large Language Models (LLMs) into schedule management systems represents a significant evolution in conversational AI assistance. This paper synthesizes recent research from 2024-2025 examining the design, implementation, and evaluation of LLM-based chatbots for scheduling tasks. Across multiple studies, researchers have explored multi-agent architectures, human-AI collaboration models, explainability mechanisms, and domain-specific applications. Key findings indicate that while LLM agents demonstrate considerable promise as daily assistants, their effectiveness depends critically on architectural choices, user trust calibration, and the integration of structured reasoning capabilities. Systems employing graph-structured multi-agent coordination and hybrid symbolic-LLM approaches show particular promise for handling the temporal reasoning and constraint satisfaction requirements inherent in schedule management. This review synthesizes current research contributions and identifies future directions for the field.
Large Language Models, conversational agents, schedule management, human-AI collaboration, multi-agent systems, explainable AI.
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