Togedule: Scheduling Meetings with Large Language Models and Adaptive Representations of Group Availability
arXiv:2505.01000v5 Announce Type: replace 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 c
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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.
Comments: This paper has been accepted at CSCW 2025
Subjects:
Human-Computer Interaction (cs.HC)
Cite as: arXiv:2505.01000 [cs.HC]
(or arXiv:2505.01000v5 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2505.01000
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1145/3757513
DOI(s) linking to related resources
Submission history
From: Jaeyoon Song [view email] [v1] Fri, 2 May 2025 04:54:52 UTC (11,400 KB) [v2] Sat, 17 May 2025 04:25:13 UTC (11,400 KB) [v3] Mon, 28 Jul 2025 06:19:51 UTC (4,597 KB) [v4] Thu, 28 Aug 2025 20:10:07 UTC (4,598 KB) [v5] Mon, 30 Mar 2026 21:12:10 UTC (4,604 KB)
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