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Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams for Effective Teamwork
Open accessAbstract
Recently, AI is integrating into teams to collaborate with humans as a teammate with the goal of achieving unprecedented team outcomes. Much of the coordination between humans and AI teammates relies on human-AI communication, which is challenging due to AI's limitations on natural language communication. Thus, it is essential to identify and develop effective communication strategies for AI teammates in human-AI teams to facilitate the coordination process. Through interviews with 60 participants who collaborated with an AI teammate in a multiplayer online game, in this paper, we explore communication strategies that humans expect AI teammates to apply to support human-AI coordination and collaboration in dyadic teaming environments, and how the AI teammate's communication can impact teaming processes. Our findings highlight four communication strategies AI teammates should apply to support their coordination with humans in dyadic teaming environments. We also find that AI teammates' proactive communication with humans could facilitate the development of human trust and situation awareness, whereas AI lacking such proactive communication is often not perceived as a teammate. Our study extends the current CSCW/HCI research on human-AI communication in teaming environments by shedding light on how communication should be structured in dyadic human-AI teams for effective and smooth collaboration.
Overview
CoChat Summary
TL;DR
Through 60 interviews after a Wizard-of-Oz dyadic human-AI teaming task in ArmA 3, this paper identifies four communication strategies humans expect AI teammates to use and shows that proactive AI communication supports trust and situation awareness, while non-proactive AI is often not perceived as a teammate. It proposes three design elements for human-AI communication in dyadic teams.
Key findings
- Humans expected AI teammates to apply four communication strategies: proactively communicating, balancing efficiency and sociability, providing immediate responses, and avoiding excessive communication once a communication pattern formed in repeated tasks.
- Proactive AI communication facilitated trust by benefiting humans’ individual performance, increasing transparency, and making the AI seem like a reliable partner; it also developed team situation awareness by informing AI progress and helping humans decide next steps.
- AI lacking proactive communication was often perceived as an individual rather than a teammate, hindering coordination; humans then relied on implicit cues such as the map or seeing the AI in the game space.
- Excessive AI communication could distract humans or interrupt their own tasks, especially after coordination routines were established.
Methods
- 60 semi-structured interviews (10 open-ended questions; total 428 minutes 30 seconds; typically 7–10 minutes each) after participants completed three rounds of an eight-minute timed crate-collection task in ArmA 3 with a Wizard-of-Oz “AI” teammate Zeus; half interacted with a proactive-communication AI and half with a non-proactive one, using text chat only, with AI communication accuracy set at 100%.
- Participants were recruited from a midsize Southeastern university subject pool; mean age was 18.58 (range 18–21); 45 (75%) played games less than 1 hour per week; 52 (86.7%) were not familiar with ArmA 3; reported NARS scores ranged from 1.42 to 3.93 (Table 1: 1.43–3.93; mean 3.05, median 3.11).
- Inductive qualitative analysis: two authors read transcripts, highlighted relevant material, independently identified themes/subthemes, then refined them with all authors.
Limitations
- Stated by the paper: the dyadic setting limits generalization to more complex team compositions; human and AI shared the same responsibility, so findings may differ with unique roles; only one specific context was examined; participants were college students averaging 19 years old and novices at the task; and AI communication was always 100% accurate, unlike real-world tasks.
- Evident from the text: the Wizard-of-Oz design means participants did not interact with an actual AI; the fixed phrase list and text-only channel constrained natural communication; and the short interviews (typically 7–10 minutes) in a game context may limit depth and generalizability.
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0 Valid · 1 Not valid · 60 Not checked · 69 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
“Our findings highlight four communication strategies AI teammates should apply to support their coordination with humans in dyadic teaming environments.”
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > Abstract
plainn = 60- 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.
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
“(4) avoiding large amounts of communication once the communication pattern has formed in repeated team interactions (RQ1).”
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > 5 DISCUSSION
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.
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
“First, AI proactively communicating with humans enables human teammates to develop an awareness of what the AI teammate is doing.”
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > 4.2.2 AI teammates' proactive communication develops situation awareness by informing AI's progress and indirectly helping humans make next-step decisions.
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.
- 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
“(3) providing quick responses;”
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > 5 DISCUSSION
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.
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
“As P56 and P1 point out, large amount of communication from the AI teammate could distract humans from doing their own task, especially when humans have to multitask”
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > 4.1.4 AI teammates should avoid providing excessive amount of communication to humans once
hedgedstates a possible 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.
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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
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > 4.2.4 AI teammates lacking proactive communication forces humans to utilize other implicit communication approaches in HATs.
“According to P23, the implicit communication (i.e., text communication) is time-consuming, whereas seeing the AI teammate in the 3D space in game provides enough information for humans to coordinate and complete team tasks.”
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > 4.2.4 AI teammates lacking proactive communication forces humans to utilize other implicit communication approaches in HATs.
“P49 (White, Non-binary, 20, NARS 2.93) also mentions that the explicit communication is inefficient whereas humans can take use of implicit communication: I just tracked his moves on the map, and looked and saw what crate he was doing.The chat took a minute to type everything out.”
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
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > 3.2 Procedure
“(3) three rounds of eight-minute team task, where participants were asked to collaborate with an AI teammate Zeus to collect as many crates as possible in numerical order within an eight-minute time limit, with a post-survey after each round of the task;”
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > 3.2 Procedure
“How would you describe your trust in Zeus across four missions?”
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
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > 3.3 Recruitment and Participants
“The reported NARS scores in our study range from 1.42 to 3.93.”
§ Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams For Effective Teamwork > 6 CONCLUSION
“Range from 1.43 to 3.93 Mean = 3.05 Median = 3.11”
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Self-citation
Metadata
- Authors
- Zhang, R., Duan, W., Flathmann, C., McNeese, N., Freeman, G., & Williams, A.
- Affiliations
- Clemson University (US)
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2023-09-28
- Volume
- 7
- Issue
- CSCW2
- Pages
- 1-31
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W4387329047
- Cited by
- 134
- References
- 97
- Citations by year
- 2026: 60 · 2025: 53 · 2024: 17 · 2023: 4
- FWCI
- 25.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
- Keywords
- human-AI teaming, human-AI communication, communication strategies, team coordination, proactive communication, trust in AI, situational awareness, online multiplayer games, AI teammates
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.
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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.
