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Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration
Open accessAbstract
The increasing importance of artificial intelligence (AI) in everyday work also means that new insights into team collaboration must be gained. It is important to research how changes in team composition affect joint work, as previous theories and insights on teams are based on the knowledge of pure human teams. Especially, when AI-based systems act as coequal partners in collaboration scenarios, their role within the team needs to be defined. With a multi-method approach including a quantitative and a qualitative study, we constructed four team roles for AI-based teammates. In our quantitative survey based on existing team role concepts (n = 1.358), we used exploratory and confirmatory factor analysis to construct possible roles that AI-based teammates can fulfill in teams. With nine expert interviews, we discussed and further extended our initially identified team roles, to construct consistent team roles for AI-based teammates. The results show four consistent team roles: the coordinator, creator, perfectionist and doer. The new team roles including their skills and behaviors can help to better design hybrid human-AI teams and to better understand team dynamics and processes.
Overview
CoChat Summary
TL;DR
The paper uses a sequential multi-method design to identify and refine team roles that AI-based teammates can fulfill in human-AI collaboration. It reports four consistent roles—coordinator, creator, perfectionist, and doer—derived from a quantitative survey and extended through expert interviews.
Key findings
- A quantitative study with 1,358 participants yielded four factors explaining 60.614% of the variance, interpreted as AI-based team roles: coordinator, creator, perfectionist, and doer; factor reliabilities ranged from 0.770 to 0.796 and AVE from 0.529 to 0.565.
- Nine expert interviews confirmed and extended the roles: the coordinator was associated with motivating, leading, assigning tasks, discussing, capturing emotions/social dynamics, and conflict resolution; the creator with finding new solutions, research, novel patterns/associations, innovative problem-solving, and expert knowledge; the perfectionist with detail, optimal solutions, analytical thinking, structuring, and validation; the doer with concrete action, practical solutions, prioritization, and distinguishing important from unimportant.
- Experts emphasized emotional and social capabilities across roles, plus analytical thinking, multitasking, knowledge contribution, decision-making, conflict resolution, time management, assessing human capabilities, and structuring results; they also warned that AI-based teammates should not take roles that are too powerful, because humans may rest on the AI’s skills.
- The paper reports similarities between the four AI-based roles and existing role concepts by Belbin, Margerison et al., and Benne and Sheats.
Methods
- Study 1: a questionnaire operationalized from Belbin (2012), Margerison et al. (1986), and Benne and Sheats (1948), containing 58 AI-teammate items plus demographic and open questions (68 total), answered on a 5-point Likert scale; it was translated into Chinese and distributed via the Tencent Survey portal between November 2019 and January 2020.
- From 2,582 collected responses, 1,358 were used after data cleansing (52% recall rate); analysis used exploratory factor analysis (principal component analysis, varimax rotation, split sample) followed by confirmatory factor analysis.
- Study 2: semi-structured interviews with nine experts selected for more than 5 years of practical experience in AI and/or team composition; all were male, German, aged 34–58; interviews took place in January–March 2021, averaged 80 min (minimum 63, maximum 95), were transcribed and coded in MaxQDA, producing 38 categories and 813 paraphrased text segments; data saturation was reached.
Limitations
- Stated in the paper: the samples are narrow; quantitative participants represent a specific target group who do not necessarily have many years of teamwork experience; cultural and national habits limit generalization; the expert panel is limited by small number and demographics (all male, one nationality, German); further experimental research is needed and is hardly feasible at this stage.
- Evident from the text: the quantitative sample is largely young (740 aged 18–24; 45% students) and was collected through a Chinese survey portal, which may further constrain generalizability; the roles are based on attitudes/perceptions and expert discussion rather than observed team behavior or experimental validation.
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0 Valid · 19 Not valid · 21 Not checked · 112 Own results and interpretations
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The analysis yielded four factors explaining a total of 60.614% of the variance for the entire set of variables.”
§ Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration > 4.1.2 Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption not shown here.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With nine expert interviews, we discussed and further extended our initially identified team roles, to construct consistent team roles for AI-based teammates.”
- Also stated in: § 1 Introduction · plain
“With the results of the interviews, we refined and extended our team roles.”
§ Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration > Abstract
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § 1 Introduction · plain
None of the sources cited for this claim have been checked yet.
In the paper
“In order to determine whether the different indicators are unrelated, we measure discriminant validity with inter-correlations between factors, which indicate low and very low correlations between each factor (see Table 6) (Rummel 1988;Raithel 2008).”
§ Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration > 4.1.2 Results
What the sources say
- Not matchedcites Rummel 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites Raithel 2008 source
hedgedstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption not shown here.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results show four consistent team roles: the coordinator, creator, perfectionist and doer.”
- Also stated in: § Abstract · plain
“we constructed four team roles for AIbased teammates.”
- Also stated in: § 1 Introduction · plain
“We identified four desired new team roles for AIbased team members, which we compared to existing team role concepts.”
§ Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration > Abstract
plainn = 1,358- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Abstract · plain
None of the sources cited for this claim have been checked yet.
In the paper
“The MSA-value of each item is on the diagonal of the anti-image-correlation matrix and the MSA-value of the items is between 0.722 and 0.959 (Rummel 1988;Mair 2018).”
§ Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration > 4.1.2 Results
What the sources say
- Not matchedcites Rummel 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites Mair 2018 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption not shown here.
- Not matched
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Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration > 4.2.1 Data collection and data analysis
“more than 5 years of practical experience in the field of AI and/or teamwork and composition of teams.”
§ Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration > 1 3
“His experience is based on two years of practical experience as a project manager with team responsibility and working in the field of AI in a mediumsized company”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration > 4 Methodology
“We excluded the nine individual roles of Benne and Sheats (1948)”
§ Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration > 3.3 Team Roles
“The third category cover individual roles, which are eight”
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Metadata
- Authors
- Siemon, D.
- Affiliations
- Lappeenranta-Lahti University of Technology (FI)
- Corresponding author
- Siemon, Dominik
- ORCID
- Siemon, Dominik
- Venue
- Group Decision and Negotiation
- Published
- 2022-07-20
- Volume
- 31
- Issue
- 5
- Pages
- 871-912
- ISSN
- 0926-2644 (ISSN-L)
- 1572-9907
- OpenAlex
- W4286008004
- Cited by
- 179
- References
- 69
- Citations by year
- 2026: 83 · 2025: 45 · 2024: 35 · 2023: 14 · 2022: 1
- FWCI
- 16.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Locations
- Group Decision and Negotiation (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- LUTPub (LUT University) (submittedVersion, cc-by)
- Funders
- LUT University (previously Lappeenranta University of Technology
- Topics
- Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- team roles, human-AI collaboration, team composition, team dynamics, coordinators, creators
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
