Remote research methods for Human–AI–Robot Teaming

Glenn J. Lematta, Christopher C. Corral, Verica Buchanan, Craig J. Johnson, Anagha Mudigonda, Federico Scholcover, Margaret E. Wong, Akuadasuo Ezenyilimba, Manuel Baeriswyl, Jimin Kim, Eric Holder, Erin K. Chiou, Nancy J. Cooke

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This study focuses on methodological adaptations and considerations for remote research on Human–AI–Robot Teaming (HART) amidst the COVID-19 pandemic. Themes and effective remote research methods were explored. Central issues in remote research were identified, such as challenges in attending to participants' experiences, coordinating experimenter teams remotely, and protecting privacy and confidentiality. Instances of experimental design overcoming these challenges were identified in methods for recruitment and onboarding, training, team task scenarios, and measurement. Three case studies are presented in which interactive in-person testbeds for HART were rapidly redesigned to function remotely. Although COVID-19 may have temporarily constrained experimental design, future HART studies may adopt remote research methods to expand the research toolkit.

Original languageEnglish (US)
Pages (from-to)133-150
Number of pages18
JournalHuman Factors and Ergonomics In Manufacturing
Issue number1
StatePublished - Jan 2022


  • COVID-19
  • Human–AI–Robot Teaming
  • remote research
  • research methods

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Industrial and Manufacturing Engineering


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