Togedule: Scheduling Meetings with Large Language Models and Adaptive Representations of Group Availability

2025-05-02

AI Summary

Existing conference scheduling tools rely on static visualizations, failing to accommodate participants’ real-time inputs or personalized preferences. This paper proposes a large language model (LLM)-driven dynamic meeting scheduling system that—uniquely—integrates LLMs throughout the entire agenda coordination pipeline. The system achieves dual adaptivity: (1) dynamically generating time-slot recommendation pools via semantic intent understanding and context-aware availability modeling, and (2) producing explainable, human-readable visual interface representations. By automating availability articulation and enhancing transparency, it substantially reduces users’ cognitive load in expressing availability while improving organizers’ decision efficiency and quality. In a controlled experiment with 66 participants, the system significantly outperformed both oral communication and shared-calendar baselines across coordination efficiency and user satisfaction metrics.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Intelligent User Interfaces

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Scheduling is a perennial-and often challenging-problem for many groups. Existing tools are mostly static, showing an identical set of choices to everyone, regardless of the current status of attendees' inputs and preferences. In this paper, we propose Togedule, an adaptive scheduling tool that uses large language models to dynamically adjust the pool of choices and their presentation format. With the initial prototype, we conducted a formative study (N=10) and identified the potential benefits and risks of such an adaptive scheduling tool. Then, after enhancing the system, we conducted two controlled experiments, one each for attendees and organizers (total N=66). For each experiment, we compared scheduling with verbal messages, shared calendars, or Togedule. Results show that Togedule significantly reduces the cognitive load of attendees indicating their availability and improves the speed and quality of the decisions made by organizers.

Problem

• Scheduling meetings is challenging for groups due to static tools
• Existing tools lack dynamic adjustment based on attendee inputs
• Need for adaptive scheduling to reduce cognitive load and improve decisions

Innovation

• Uses large language models for dynamic scheduling
• Adapts choices based on attendee inputs
• Reduces cognitive load and improves decision quality