Togedule: Scheduling Meetings with Large Language Models and Adaptive Representations of Group Availability
For groups struggling with scheduling, Togedule offers an adaptive tool that outperforms static calendars and verbal messages, though the improvements are incremental.
Togedule uses large language models to dynamically adjust meeting scheduling options based on attendee inputs, reducing cognitive load for attendees and improving decision speed and quality for organizers.
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.