Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

One Is Not Enough: How People Use Multiple AI Models in Everyday Life

Seunghwa Pyo, Donggun Lee, Jungwoo Rhee, Soobin Park, Youn-kyung Lim

Abstract

People increasingly use multiple Multimodal Large Language Models (MLLMs) concurrently, selecting each based on its perceived strengths. This cross-platform practice creates coordination challenges: adapting prompts to different interfaces, calibrating trust against inconsistent behaviors, and navigating separate conversation histories. Prior HCI research focused on single-agent interactions, leaving multi-MLLM orchestration underexplored. Through a diary study and semi-structured interviews (N=10), we examine how individuals organize work across competing AI systems. Our findings reveal that users construct primary and secondary hierarchies among models that shift over usage context. They also develop personalized switching patterns triggered by task aggregation to adjust effort and latency, and output credibility. These insights inform future tool design opportunities, supporting users to coordinate multi-MLLM workflows.

One Is Not Enough: How People Use Multiple AI Models in Everyday Life

Abstract

People increasingly use multiple Multimodal Large Language Models (MLLMs) concurrently, selecting each based on its perceived strengths. This cross-platform practice creates coordination challenges: adapting prompts to different interfaces, calibrating trust against inconsistent behaviors, and navigating separate conversation histories. Prior HCI research focused on single-agent interactions, leaving multi-MLLM orchestration underexplored. Through a diary study and semi-structured interviews (N=10), we examine how individuals organize work across competing AI systems. Our findings reveal that users construct primary and secondary hierarchies among models that shift over usage context. They also develop personalized switching patterns triggered by task aggregation to adjust effort and latency, and output credibility. These insights inform future tool design opportunities, supporting users to coordinate multi-MLLM workflows.

Paper Structure

This paper contains 16 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Diary Study and Post-study Interview
  4. Participants
  5. Data Collection and Analysis
  6. Findings
  7. Individual MLLM Hierarchy Structures
  8. Different Hierarchy Across Personal and Work Contexts
  9. Factors Shaping Model Hierarchies
  10. MLLM Coordination Strategies
  11. Switch Model with Purpose
  12. Don't Migrate, Iterate Instead
  13. Discussion
  14. Limitations and Future Work
  15. Conclusion
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Three recurring hierarchy patterns: (a) Cross-Context Primay, (b) Work-Focused, and (c) Split-Primary.