What Types of Human-AI Teams Exist?

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Nathan Hughes, Ibrahim Habli
• Venue: arXiv: cs.HC
• Published: 2026-07-02
• Collected: 2026-07-03
• URL: http://arxiv.org/abs/2607.02198v1
• PDF: https://arxiv.org/pdf/2607.02198v1
• DOI: UNKNOWN

Plain-Language Brief

Researchers analyzed 53 papers about human-AI teams and found that there are five main clusters or types of teams: AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity. Each cluster has unique characteristics, raising questions about the transferability of insights between studies.

What the Document Says

• The study focused on human-AI teaming in multiple domains.
• 53 papers on human-AI teams were analyzed.
• The papers were categorized into five main clusters based on psychological taxonomies of teaming.
• The five clusters are: AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity.
• Each cluster represents a unique combination of holistic team-level characteristics.
• Insights might not be transferable between papers due to different team types.
• The study provides guidance on identifying the types of human-AI teams studied.

Key Concepts Mentioned

• human-AI teams
• psychological taxonomies
• team-level characteristics
• insights

Claims Made by the Document

• There are five main clusters or types of human-AI teams.
• Each cluster has unique characteristics.
• Insights might not be transferable between papers due to different team types.

Evidence Used

• Analysis of 53 papers on human-AI teams
• Categorization into five main clusters based on psychological taxonomies

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Human-AI teaming has received increasing attention in the literature. However, the range of studies conducted in multiple domains make it difficult to understand what types of teams are being studied, and in what ways are they similar/different from one another. In this study, we analyse 53 papers on human-AI teams and categorise them into five main clusters based on psychological taxonomies of teaming; AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity. Each cluster represents a unique combination of holistic team-level characteristics, indicating there are multiple disparate team types studied under the same definition. In turn, this raises the question of whether insights are truly transferable between papers. We conclude with guidance on how to identify the types of human-AI teams studied, a checklist for reporting a human-AI team in research work, and ways in which the field can be further synthesised.