[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83639-en":3,"doc-seo-83639-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83639,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","What Types of Human-AI Teams Exist?","Human-AI teaming has gained increasing attention, yet the literature spans many domains, making it difficult to determine what team types are studied and how they differ. This scoping review analyzes 53 human-AI team papers and clusters them into five categories based on psychological taxonomies: AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity. The clusters reflect distinct team-level characteristics, limiting transferability of insights across papers. The work provides guidance for identifying team types, a reporting checklist, and directions for synthesizing the field.","arXiv :2607 .02 198v 1 [ cs .HC] 2 Jul 2026  \nWhat Types of Human-AI Teams Exist?  \nNathan Hughes 1* and Ibrahim Habli 1  \n1* Centre for Assuring Autonomy, University of York, Deramore Lane,  \nYork, North Yorkshire, YO10 5DD, UK.  \n*Corresponding author(s). E-mail(s): [nathan.hughes@york.ac.uk](nathan.hughes@york.ac.uk) ;  \nContributing authors: [ibrahim.habli@york.ac.uk](ibrahim.habli@york.ac.uk) ;  \nAbstract  \nHuman-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.  \nKeywords: Human-AI Teaming, teaming classification, experimental analysis,  \nscoping review  \n1 Introduction  \nHuman-AI teaming as a field has grown exponentially popular in recent years, and broadly refers to a team consisting of one or more humans and one or more AI working together interdependently towards a shared goal [1] . To do so, each entity must act as a team member, and possess unique and complementary capabilities. In other words, human-AI teaming aims to capitalise on the complementary strengths of both humans and AI, with the goal of improving overall team performance, across several applications. For example, it has been studied in numerous domains, ranging from  \n1  \nsafety-critical systems such as healthcare (e.g. [2]), work-based settings (e.g. [3]), and recreational settings such as video games (e.g. [4]) .  \nGiven this explosion of interest, there have been recent reviews on the human-AI teaming literature, such as [1] . However, such reviews reveal many human-AI teaming papers focus heavily on the ‘AI’ technical aspect, rather than on the ’team’ aspect. This represents an important gap in understanding, as teaming is the concept that separates human-AI teaming from other related terminologies, such as human-AI collaboration [5] and AI decision-support tools [6] . Further, knowing who is involved in a team is only one component of what distinguishes and explains a team [7], and in particular overlooks which holistic characteristics explain currently studied human-AI teams.  \nThis leads to two problems. Firstly, it is difficult to extract concrete examples of when an AI is no longer a ‘mere tool’ and instead perceived as a teammate from existing reviews. In turn, the specificity of the term is difficult to explain and use to separate different human and AI contexts. Secondly, and more crucially, it is not clear what types of teams are studied within human-AI teaming from a teaming perspective. In particular, it is unclear what types of tasks are pursued by humans and AI, and in what ways are team members organised to allow for collaborative working. This is important to consider, as the wide range of application domains referenced earlier makes it unclear what these disparate environments have in common, if anything. Overall, it has become unclear what is specific about human-AI teaming, and in turn what specifically has been studied under this umbrella definition. In turn, it is difficult to understand what is cohesive about this line of research, and how effectively insights can be shared across papers.  \nTo resolve these issues in definitional u","cbCaicC1ZT4Y7Zx6","https://ap.wps.com/l/cbCaicC1ZT4Y7Zx6","pdf",3682483,2,1,36,"English","en",105,"# Abstract\n# Introduction\n## Problem framing and definitional uncertainty\n## Contributions and taxonomy overview","[{\"question\":\"How many papers are analyzed, and what is the purpose of the analysis?\",\"answer\":\"The study analyzes 53 experimental papers to categorize the types of human-AI teams examined in the literature and clarify how they differ from one another.\"},{\"question\":\"What are the five main clusters of human-AI teams identified?\",\"answer\":\"The five clusters are AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity.\"},{\"question\":\"Why might findings from different human-AI teaming papers be difficult to compare or transfer?\",\"answer\":\"Because the identified clusters represent distinct combinations of holistic team-level characteristics, implying that teams studied under the same overall definition are not inherently interchangeable.\"}]",1784189440,91,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"what-types-of-human-ai-teams-exist","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/what-types-of-human-ai-teams-exist/83639/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How many papers are analyzed, and what is the purpose of the analysis?","Question",{"text":75,"@type":76},"The study analyzes 53 experimental papers to categorize the types of human-AI teams examined in the literature and clarify how they differ from one another.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the five main clusters of human-AI teams identified?",{"text":80,"@type":76},"The five clusters are AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity.",{"name":82,"@type":73,"acceptedAnswer":83},"Why might findings from different human-AI teaming papers be difficult to compare or transfer?",{"text":84,"@type":76},"Because the identified clusters represent distinct combinations of holistic team-level characteristics, implying that teams studied under the same overall definition are not inherently 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