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Beyond the Desk: Barriers and Future Opportunities for AI to Assist Scientists in Embodied Physical Tasks

Irene Hou, Alexander Qin, Lauren Cheng, Philip J. Guo

Abstract

More scientists are now using AI, but prior studies have examined only how they use it 'at the desk' for computer-based work. However, given that scientific work often happens 'beyond the desk' at lab and field sites, we conducted the first study of how scientific practitioners use AI for embodied physical tasks. We interviewed 12 scientific practitioners doing hands-on lab and fieldwork in domains like nuclear fusion, primate cognition, and biochemistry, and found three barriers to AI adoption in these settings: 1) experimental setups are too high-stakes to risk AI errors, 2) constrained environments make it hard to use AI, and 3) AI cannot match the tacit knowledge of humans. Participants then developed speculative designs for future AI assistants to 1) monitor task status, 2) organize lab-wide knowledge, 3) monitor scientists' health, 4) do field scouting, 5) do hands-on chores. Our findings point toward AI as background infrastructure to support physical work rather than replacing human expertise.

Beyond the Desk: Barriers and Future Opportunities for AI to Assist Scientists in Embodied Physical Tasks

Abstract

More scientists are now using AI, but prior studies have examined only how they use it 'at the desk' for computer-based work. However, given that scientific work often happens 'beyond the desk' at lab and field sites, we conducted the first study of how scientific practitioners use AI for embodied physical tasks. We interviewed 12 scientific practitioners doing hands-on lab and fieldwork in domains like nuclear fusion, primate cognition, and biochemistry, and found three barriers to AI adoption in these settings: 1) experimental setups are too high-stakes to risk AI errors, 2) constrained environments make it hard to use AI, and 3) AI cannot match the tacit knowledge of humans. Participants then developed speculative designs for future AI assistants to 1) monitor task status, 2) organize lab-wide knowledge, 3) monitor scientists' health, 4) do field scouting, 5) do hands-on chores. Our findings point toward AI as background infrastructure to support physical work rather than replacing human expertise.
Paper Structure (28 sections, 3 figures, 1 table)

This paper contains 28 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Studies of Scientists' Workflows
  4. Human-Centered AI in Science
  5. Human-Centered AI in General Knowledge Work
  6. Methods
  7. Participants
  8. Design Rationale for Our Situated Interview Protocol
  9. Data Analysis
  10. Study Scope and Limitations
  11. Findings: How Lab and Field Scientific Practitioners Currently Use AI Tools
  12. AI Tools for Knowledge Work 'At the Desk'
  13. AI Tools for Physical Work 'Beyond the Desk'
  14. Findings: Barriers that Discourage Lab and Field Scientific Practitioners from Adopting AI in their Workflows
  15. Scientific practitioners do not want to face risk of AI errors since physical experimental setups are too high-stakes
  16. ...and 13 more sections

Figures (3)

  • Figure 1: To find barriers and opportunities for AI adoption, we visited workplaces and performed on-site interviews with 12 scientific practitioners in a range of lab and field settings such as (A) behavioral neuroscience labs with surgery and bench areas (P1, P5), (B) field site for a primate behavioral scientist (P8), (C) cellular biochemistry lab (P12).
  • Figure 2: Thematic maps summarizing (top) the three categories of barriers that discourage lab and field scientific practitioners from adopting AI, and (bottom) five speculative AI assistant archetypes organized into mid-level conceptual clusters.
  • Figure 3: Five archetypes that are representative of speculative designs that our 12 participants envisioned for how future AI could help lab and field scientists: (A) as the lab's collective knowledge keeper, (B)-(C) distributed lab-wide task status monitor, (D) real-time monitor for scientists' cognitive and physical health, (E) mobile scout for fieldwork, (F) collaborator for hands-on physical chores. (Image credit: all illustrations were drawn by human artist Lauren Cheng without AI assistance.)