The DOI matches its Crossref record; 155 of the 155 references it cites were found in a registry or an index; none of the 252 sources its claims cite is checked for support yet.
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature
Open accessAbstract
Objective: We define human–autonomy teaming and offer a synthesis of the existing empirical research on the topic. Specifically, we identify the research environments, dependent variables, themes representing the key findings, and critical future research directions. Background: Whereas a burgeoning literature on high-performance teamwork identifies the factors critical to success, much less is known about how human–autonomy teams (HATs) achieve success. Human–autonomy teamwork involves humans working interdependently toward a common goal along with autonomous agents. Autonomous agents involve a degree of self-government and self-directed behavior (agency), and autonomous agents take on a unique role or set of tasks and work interdependently with human team members to achieve a shared objective. Method: We searched the literature on human–autonomy teaming. To meet our criteria for inclusion, the paper needed to involve empirical research and meet our definition of human–autonomy teaming. We found 76 articles that met our criteria for inclusion. Results: We report on research environments and we find that the key independent variables involve autonomous agent characteristics, team composition, task characteristics, human individual differences, training, and communication. We identify themes for each of these and discuss the future research needs. Conclusion: There are areas where research findings are clear and consistent, but there are many opportunities for future research. Particularly important will be research that identifies mechanisms linking team input to team output variables.
Overview
CoChat Summary
TL;DR
This integrative review synthesizes 76 empirical articles on human–autonomy teaming (HAT), defines HATs as interdependent human(s) and autonomous agent(s) occupying distinct team roles, and maps findings using an I-M-O framework. It finds generally positive effects of higher agent autonomy, reliability, interdependence, and training, but human–human teams usually outperform HATs and transparency/communication quantity show mixed effects.
Key findings
- The review identified 76 empirical articles; all were simulation/lab studies, none field-based. Of these, 57 were military- or emergency-related simulations, 17 were nonmilitary/emergency simulations, and 2 could not be classified; the most represented testbeds were CERTT-UAS-STE (18 articles) and MIX (15 articles).
- Agent autonomy was coded as high in 26 articles, partial in 31, and multiple levels in 15. Higher agent autonomy generally had positive effects (e.g., easier to work with, higher communication efficiency, stronger coordination, higher performance, lower workload), though some studies found a moderate level ideal; reliability was consistently positive, while transparency had mixed effects (e.g., improved performance/trust/understanding but also complacency, conflict, and workload).
- Human–human teams generally outperformed HATs; interdependence led to positive outcomes; higher task difficulty led to negative outcomes such as higher workload and lower performance; training produced consistently positive results; communication quality was associated with stronger team functioning, but communication quantity findings were mixed.
- Dependent variables studied most often were team and individual performance (70 studies), workload (39), trust (24), situation awareness (23), team coordination (15), and shared mental models (6); little research addressed Hackman’s team viability and individual learning/growth outcomes.
Methods
- Narrative/integrative literature review with four-step search. Inclusion required empirical research, sufficient description to code agent autonomy, and at least one autonomous agent and one human working interdependently toward a common goal; the agent had to reach at least Level 5 on Parasuraman et al.’s (2000) automation continuum.
- Searches covered ACM Digital Library, IEEE Xplore, and Sage Journals: initial search returned 1,942 articles, reduced to 476 after full-text “team” filtering, yielding 33 included; author-focused search returned 930 potential articles, yielding 31 included; Google Scholar search returned 152 articles, yielding 8 included; adding “human–automation interaction” yielded 4 more; total unique articles = 76.
- Coding was inductive and iterative, organized within the I-M-O model of team effectiveness. After calibration, 30% of remaining articles were randomly selected for independent coding; inter-coder agreement was 89% overall.
Limitations
- Stated: the search may have missed relevant articles; using “teamwork” as a search term may have constrained coverage; a disproportionate number of papers came from two laboratories/testbeds (CERTT-UAS-STE 24%, MIX 20%); it is unclear whether humans perceived the autonomous agents as team members; and HRI and autonomous vehicle literatures were omitted.
- Evident from the text: all included studies were simulation/lab-based, most involved short-term “zero-history” teams, often single human–single agent dyads, limited communication, and mostly partial rather than high autonomy, limiting generalizability.
- Evident from the text: no studies formally reported mediation analyses, so mechanisms linking team inputs to outputs remain largely untested.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
0 Valid · 10 Not valid · 200 Not checked · 136 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
“We report on research environments and we find that the key independent variables involve autonomous agent characteristics, team composition, task characteristics, human individual differences, training, and communication.”
§ Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature > Abstract > 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.
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
“We found 76 articles that met our criteria for inclusion.”
§ Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature > Abstract > Method
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most individual studies in the current review only considered a single path in the I-M-O chain (i.e., did not test mediating mechanisms).”
§ Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature > Method > Coding Framework Development
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.
In the paper
“(II) Cultural variables have an impact on user trust in the autonomous agent.”
§ Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature > Results of Literature Review: Research Environments, Themes, and Future Research Opportunities
What the sources say
- Not matchedcites Chien et al. (2016)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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
“| Team coordination | 15 |”
§ Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature > Results of Literature Review: Research Environments, Themes, and Future Research Opportunities
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
179 more claims
Create a free account to see all 346 claims and to verify the 150 references still open.
- Probably not a substantial claim. 118
- It may state two separate claims as one. 80
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
§ Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature > Introduction > Defining Autonomy and Human–Autonomy Teams (HAT)
“Partial levels of agent autonomy can spontaneously recommend and execute actions unless they are vetoed (Levels 5–6).”
§ Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature > Introduction > Defining Autonomy and Human–Autonomy Teams (HAT)
“At Levels 5–6, Parasuraman et al. (2000) noted that the automation will generate decisions and actions that are communicated to the human(s) and then will execute with approval (Level 5) or with limited time for veto before the actions are implemented autonomously (Level 6).”
Citations
Cites
(1994) · Journal of the American Dietetic Association · doi:10.1016/0002-8223(94)92216-0 · Cited by 45,910
TensorFlow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08695 · Cited by 11,822
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,583
Culture's Consequences: International Differences in Work-Related Values
Gladwin, T. N., & Hofstede, G. (1981) · The Academy of Management Review · doi:10.2307/257651 · Cited by 9,275
Introduction to Statistical Mediation Analysis
MacKinnon, D. P. (2012) · doi:10.4324/9780203809556 · Cited by 5,618
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Lee, J., Bagheri, B., & Kao, H.-A. (2015) · Manufacturing Letters · doi:10.1016/j.mfglet.2014.12.001 · Cited by 5,113
Humans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 12×
“Between Sheridan’s (1980) levels of automation and the taxonomy of automation types proposed by Parasuraman et al. (2000), there are existing taxonomies that relate to the notion of varying levels of agency among automation.”
BackgroundMethodInfluentialA Temporally Based Framework and Taxonomy of Team Processes
Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001) · The Academy of Management Review · doi:10.2307/259182 · Cited by 3,659
Groups: Interaction and Performance.
Polley, R. B., & McGrath, J. E. (1984) · Administrative Science Quarterly · doi:10.2307/2393041 · Cited by 3,290
Research works printed but not linked: 27
- Abadi M., 2016, 12th Symposium on Operating Systems Design and Implementation, 16, 265
- *Boyce M. W., 2015, Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (pp. 179–180).
- Byrne D. E, 1971, The attraction paradigm
- *Chen J. Y., 2013, [Conference session]. 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 103–104)
- *Chen J. Y., 2016, Companion Publication of the 21st International Conference on Intelligent User Interfaces (IUI) (pp. 28–31).
- *Chien S., 2016, In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp.2884–2889).
- *Demir M., 2016, 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) (pp. 28–34). IEEE
- *Demir M., 2019, 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) (pp. 122–126).
- Endsley M. R., 2012, Designing for situation awareness: An approach to human-centered design
- Endsley M. R., 1995, Situation awareness global assessment technique (SAGAT) TRACON air traffic control version user guide
- Fitts P. M, 1951, Human engineering for an effective air-navigation and traffic-control system
- Hackman J. R, 1990, Groups that work and those that don't (No E10 H123)
- *Hafızoğlu F. M., 2018, Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) (pp. 514–522).
- *Hanna N., 2014, Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems (pp. 1485–1486).
- Hanna N., 2014, Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (pp.1369–1370).
- *Hanna N., 2015, Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems (pp. 1777–1778).
- Kazi S., Human Factors.
- Kidd C. D., 2005, Proceeding of AISB, 5, 141
- 10.1037/0021-9010.61.4.395 · doi:10.1037/0021-9010.61.4.395 · Paper page
- Powers A., 2007, Proceedings of the Acm/ieee International Conference on Human-Robot Interaction (pp. 145–152).
- Reig S., 2019, 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 742–744).
- Sheridan T. B, 1980, Technology Review, 83, 60
- 10.1177/014920639902500205 · doi:10.1177/014920639902500205 · Paper page
- Woods D. D, 1985, Conference Record for 1985 IEEE Third Conference on Human Factors and Nuclear Safety
- Woods D. D, 1994, Proceedings of the 16th Annual Conference of the Cognitive Science Study. Hillsdale, NJ: Lawrence Erlbaum (pp. 961–971).
- Woods D. D. (1996). Decomposing automation: Apparent simplicity, real complexity (pp. 3–17). Theory and applications.
- You S., 2018, International Journal of Robotic Engineering, (IJRE), 2
Cited by
Items 201–300 of 694
Jôrgensen, B. N., & Ma, Z. G. (2026) · Energies · doi:10.3390/en19061472 · Cited by 7
Context-dependent preferences for a decision support system's level of automation
Schilling, T., Müller, R., Ellwart, T., & Antoni, C. H. (2023) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2023.100350 · Cited by 7
A systematic review of technology in the after-action review (or debrief)
Keiser, N. L. (2024) · Organizational Psychology Review · doi:10.1177/20413866241245314 · Cited by 7
Gil, R. S., Ulfert, A., Rispens, S., & Blanc, P. M. L. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-65411-4_31 · Cited by 7
Sherrill, A. M., Wiese, C. W., Abdullah, S., & Arriaga, R. I. (2025) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-025-00484-4 · Cited by 7
A conceptual framework for human–AI collaborative genome annotation
Li, X., Whan, A., McNeil, M., Starns, D., Irons, J. L., Andrew, S. C., & Suchecki, R. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf377 · Cited by 7
Contrastive Learning-Based Agent Modeling for Deep Reinforcement Learning
Ma, W., Chang, Y., Yang, J., Wang, Y., & Lin, C. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3595684 · Cited by 7
Farah, Y. A., & Dorneich, M. C. (2024) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813241269252 · Cited by 7
Xu, X., Yu, R., Yuan, M., & Zheng, J. (2025) · Ergonomics · doi:10.1080/00140139.2025.2456535 · Cited by 7
A New Experimental Paradigm to Manipulate Risk in Human-Automation Research
Hoesterey, S., & Onnasch, L. (2022) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208221133878 · Cited by 7
Developing A Dynamic Testbed: Designing for Trust in Complex Human Agent Teams
Nguyen, D., Carmody, K., Wildman, J. L., & Carroll, M. B. (2022) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms56717.2022.9980750 · Cited by 7
Collaborative Appropriation of AI in the Context of Interacting with AI
Herrmann, T. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35894-4_18 · Cited by 7
Wallinheimo, A.-S., Evans, S., & Davitti, E. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1253940 · Cited by 7
Interpersonal influence matters: Trust contagion and repair in human-human-AI team
Rojas, E., Hsu, D., Huang, J., & Li, M. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100194 · Cited by 6
Dell’Anna, D., Murukannaiah, P. K., Yurrita, M., Dudzik, B., Grossi, D., Jonker, C. M., Oertel, C., & Yolum, P. (2026) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-025-09730-8 · Cited by 6
The big five model of teamwork and human autonomy teams: a scoping review
Salmon, P. M., King, B. J., Hall, D., McLean, S., Thompson, J., Cooke, N. J., Salas, E., Loft, S. D., & Read, G. J. M. (2026) · Applied Ergonomics · doi:10.1016/j.apergo.2026.104761 · Cited by 6
Patole, S. R. (2024) · Performance Improvement Quarterly · doi:10.56811/piq-24-0039 · Cited by 6
Performance rather than reputation affects humans’ trust towards an artificial agent
Becker, F., Spannagl, C. I., Buder, J., & Huff, M. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100122 · Cited by 6
Experimental evaluation of cognitive agents for collaboration in human-autonomy cyber defense teams
Du, Y., Prébot, B., Malloy, T., Fang, F., & González, C. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100148 · Cited by 6
Navigating Decisions in the Cockpit: The Intelligent Pilot Advisory System
Djartov, B., & Würfel, J. (2025) · Progress in IS · doi:10.1007/978-3-031-83512-4_18 · Cited by 6
Narayanan, R., Cohen, M. C., Feigh, K. M., & Cooke, N. J. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251358788 · Cited by 6
Cultural Tightness Increases Individuals’ Preference for Control in Human–AI Collaboration
Sui, J., Zhang, Y., & Zhao, Y. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2530079 · Cited by 6
Human-Machine Intelligence: Frigates are Intelligent Organisms
Rockbach, J. D., & Witte, T. E. F. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9658640 · Cited by 6
Hirose, J., Kotani, K., & Managi, S. (2023) · Economic Analysis and Policy · doi:10.1016/j.eap.2023.06.001 · Cited by 6
Reducing the Costs of Automation Failure by Providing Voluntary Automation Checking Tools
Bowden, V. K., Long, D., & Loft, S. D. (2023) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208231190980 · Cited by 6
Sawant, S., Brady, C., Mallick, R., McNeese, N. J., Madathil, K. C., & Bertrand, J. (2023) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/21695067231192423 · Cited by 6
Self-citationDesigning High-Impact Experiments for Human–Autonomy / AI Teaming
Flathmann, C., McNeese, N. J., & O’Neill, T. (2025) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434251327697 · Cited by 6
Self-citationFiore, S. M., Johnson, M., Robertson, P., Diego‐Rosell, P., & Fouse, A. S. (2023) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/21695067231192245 · Cited by 6
Patton, C. E., Wickens, C. D., Smith, C. A. P., Noble, K. M., & Clegg, B. A. (2023) · Cognitive Research Principles and Implications · doi:10.1186/s41235-023-00519-5 · Cited by 6
Expanding Human Response to Automation Failures to Sociotechnical Systems
Cooke, N. J. (2024) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434241229124 · Cited by 6
Schrills, T., Kargl, S., Bickel, M., & Franke, T. (2022) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/3n95u · Cited by 6
The Participation GamePreprint
Kennedy, M., & Phillips, N. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.12700 · Cited by 6
Virtual Teams: Taking Stock and Moving Forward
Handke, L., Costa, P., & O’Neill, T. (2024) · Small Group Research · doi:10.1177/10464964241274129 · Cited by 6
Self-citationEvaluating Cross-Training’s Impact on Perceived Teaming Outcomes for Human-AI Teams
Lancaster, C., Gilreath, H., Mallick, R., & McNeese, N. J. (2024) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813241262033 · Cited by 5
Self-citationMixed-Reality Testbed for Autonomous Fixed-Wing UAVs Human-Autonomy Teaming
Sribunma, W., Lin, L.-Y., Pant, K. A., Deng, C., Goppert, J., Brunswicker, S., & Roberts, J. (2024) · IEEE International Conference on Assured Autonomy (ICAA) · doi:10.1109/icaa64256.2024.00022 · Cited by 5
Graf, B. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108736 · Cited by 5
Towards Teams being Led by a Conversational Agent
Simpson, J., Stening, H., Nalepka, P., Dras, M., Kallen, R. W., Hosking, S. G., Richardson, M. J., & Richards, D. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3736614 · Cited by 5
Han, J., & Ren, R. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1636906 · Cited by 5
Schelble, B. G., Textor, C., Zhang, R., Lopez, J., Taverez, N., Ku, C., McNeese, N. J., Pak, R., Freeman, G., Tossell, C. C., et al. (2025) · Ergonomics · doi:10.1080/00140139.2025.2570300 · Cited by 5
Self-citationAI teammates and human performance: Evidence for commitment deficits
Winter, J. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100828 · Cited by 5
The Enterprising and Elusive Prospects of Human-AI Collaboration
Gadiraju, U., & Balayn, A. (2025) · Springer eBooks · doi:10.1007/978-3-032-01940-0_7 · Cited by 5
Grabowski, M., Morgan, G., McGarvey, J., Roberts, S., Squire, R., Ibanez, S., Bringsjord, S., & Rowen, A. (2025) · Drones · doi:10.3390/drones9120841 · Cited by 5
Attig, C., Winzer, L., Schrills, T., Zoubir, M., Mortaga, M., Wollstadt, P., Wiebel-Herboth, C. B., & Franke, T. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100246 · Cited by 5
Human-autonomy-teaming in e-sports: An exploratory study
Nols, T., Ulfert, A., & Gevers, J. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100919 · Cited by 5
Organizational Practices and Socio-Technical Design of Human-Centered AI
Herrmann, T. A. (2025) · Springer eBooks · doi:10.1007/978-981-97-8440-0_66-1 · Cited by 5
Human–AI complementarity needs augmentation, not emulation
Lin, Z. (2026) · Nature Reviews Psychology · doi:10.1038/s44159-026-00536-3 · Cited by 5
Perceived trustworthiness and moral competence of a GenAI-enabled ethical robot advisor
Momen, A., Tossell, C. C., Niemeyer, R. E., Walliser, J. C., Tolston, M. T., Funke, G. J., & de Visser, E. J. (2025) · Interaction Studies Social Behaviour and Communication in Biological and Artificial Systems · doi:10.1075/is.25072.mom · Cited by 5
Team Member or Tool? The Role of Framing in Human-Autonomy Teaming
Hagemann, V., Rieth, M., Watermann, L., Appelganc, K., & Heinicke, C. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2634249 · Cited by 5
Linking AI agent architectures to trust antecedents in human-autonomous teams: a scoping review
Zamecnik, A., Febriantoro, W., Cukurova, M., Joksimovíc, S., Großmann, G., & Siemens, G. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11572-z · Cited by 5
Dong, A., Li, P., Chen, Y., Gibson, S., Zhao, L., & He, M. (2026) · Sustainability · doi:10.3390/su18115313 · Cited by 5
Brereton, M. F., Ambe, A. H., Lovell, D. R., Sitbon, L., Capel, T., Soro, A., Xu, Y., Moreira, C., Favre, B., & Bradley, A. P. (2023) · Australian Conference on Computer-Human Interaction (OzCHI) · doi:10.1145/3638380.3638435 · Cited by 5
Exploring Human-Autonomy Teams in Automated Driving System Operations
Correa-Jullian, C., Ramos, M. A., Mosleh, A., & Ma, J. (2024) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms59971.2024.10555762 · Cited by 5
Schröder, F., Heinrich, F., & Kopp, S. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1532693 · Cited by 5
Dolata, M., & Aleya, K. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06516-3_2 · Cited by 5
RL-HAT: A New Framework for Understanding Human-Agent Teaming
Meimandi, K. J., Bolton, M. L., & Beling, P. A. (2023) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v1i1.27480 · Cited by 5
Kim, J. G., Gonzalo, J. D., Chen, I. L., Vo, A. T., Lupi, C. S., Hyderi, A. A., Haidet, P., DeWaters, A. L., Blatt, B., Holmboe, E. S., et al. (2023) · Academic Medicine · doi:10.1097/acm.0000000000005619 · Cited by 5
HOTSPOT: An ad hoc teamwork platform for mixed human-robot teams
Ribeiro, J. G., Henriques, L. M., Colcher, S., Duarte, J. C., Melo, F. S., Milidiú, R. L., & Sardinha, A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305705 · Cited by 5
Post-Public AI: Research in Groups and Teams
Bezrukova, K., & Griffith, T. L. (2025) · Small Group Research · doi:10.1177/10464964251363458 · Cited by 5
Verhagen, R., Neerincx, M. A., & Tielman, M. L. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732157 · Cited by 4
PATE Model: A 30-Year Review and Analysis of Gestural Interaction Research
Chen, X., Zhang, J., Zhao, Y., Chen, Q., Chen, B., Xu, N., Jin, E., Shen, Y., Tian, Y., Shen, M., et al. (2025) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208251359299 · Cited by 4
Rindfuss, A., Leary, S., Dutta, P., Chen, R., Clark, T. K., Kong, Z., & Anderson, A. P. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1624777 · Cited by 4
The spread of trust and distrust in human-AI teams
Zhou, S., Duan, W., Yin, X., Scalia, M. J., Hao, R., Weng, N., Funke, G. J., Tolston, M. T., Freeman, G., Schelble, B. G., et al. (2025) · Applied Ergonomics · doi:10.1016/j.apergo.2025.104648 · Cited by 4
Self-citationTowards Adaptive Autonomous Vehicle Systems: Considering Trust and Risk Perception during Failures
Lim, C., & Rajivan, P. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108267 · Cited by 4
Lancaster, C., Flathmann, C., Hsu, J., McNeese, N. J., O’Neill, T., & Salas, E. (2025) · Journal of Organizational Behavior · doi:10.1002/job.70036 · Cited by 4
Self-citationSchirmer, M., Fußwinkel, S., Mütze‐Niewöhner, S., Nitsch, V., & von Nitzsch, R. (2026) · Group Decision and Negotiation · doi:10.1007/s10726-026-09966-z · Cited by 4
Mukhopadhyay, A., Salubre, K., Javed, H., Mehrotra, S., & Akash, K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791592 · Cited by 4
When tech makes the team: Mind perception as a unifying framework for human and human-agent teams
Harris, A. M., & Larson, L. (2026) · Computers in Human Behavior · doi:10.1016/j.chb.2026.109000 · Cited by 4
Chang, Y.-L. (2026) · Journal of Organizational Effectiveness People and Performance · doi:10.1108/joepp-09-2025-0781 · Cited by 4
Constitutive vs. Corrective: A Causal Taxonomy of Human Runtime Involvement in AI Systems
Baum, K. L., & Laux, J. M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-41269-0_8 · Cited by 4
Shuo, W., Mei, Y.-Q., Yue, J.-C., & Xia, H.-M. (2026) · Information Technology & Tourism · doi:10.1007/s40558-026-00384-0 · Cited by 4
Integrating Agent Transparency Adaptation Mechanisms into Human Machine Cooperation
Simon, L., Guerin, C., Pacaux‐Lemoine, M., & Rauffet, P. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831996 · Cited by 4
Christoforakos, L., Kolb, A., & Diefenbach, S. (2024) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3679318.3685339 · Cited by 4
No laughing matter: How autonomous agents can improve well-being and teamwork
Linhardt, R. M., Berger, L., & Salas, E. (2024) · Acta Astronautica · doi:10.1016/j.actaastro.2024.10.039 · Cited by 4
Shank, D. B., Dew, M., & Sajjad, F. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2403651 · Cited by 4
Isles of autonomy: the rise of intelligent technologies
Hancock, P. A. (2025) · Ergonomics · doi:10.1080/00140139.2024.2447863 · Cited by 4
Trust in the Team as a Function of Trust in Individual Agents: Scale Validation and Modeling
Chung, H., & Yang, X. J. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251358794 · Cited by 4
Raz, A. E., Inbar, Y., Avnoon, N., Lifshitz-Milwidsky, L. B., Hofman, B., Honig, A., & Paz, Z. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1651934 · Cited by 4
Alignment in Decision-Making Attributes Predicts Trust and Delegation to AI Systems
Summerville, A. L., de Visser, E. J., McVay, J., Martí, L., Leung, A., & Widmer, C. L. (2025) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434251390012 · Cited by 4
Enabling ethics mechanisms in the governance of algorithmic artificial persons (ALAP)
Poti, S. P., Stanton, C. J., & Stevens, C. J. (2025) · Journal of Responsible Technology · doi:10.1016/j.jrt.2025.100143 · Cited by 4
Ding, H., Shen, Y., Chen, J., & Wang, P. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.105046 · Cited by 4
Work Autonomy and Adaptive Digital Assistance in Flexible Working Environments
Gensler, E., Oestreich, H., Abendroth, A., Wrede, S., & Wrede, B. (2023) · Springer eBooks · doi:10.1007/978-3-031-26104-6_7 · Cited by 4
Transparent Communications for Human–Machine Teaming
Chen, J. Y. C. (2023) · Wiley eBooks · doi:10.1002/9781119863663.ch10 · Cited by 4
Cooperative Bayesian Optimization for Imperfect Agents
Khoshvishkaie, A., Mikkola, P., Murena, P.-A., & Samuel Kaski, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43412-9_28 · Cited by 4
Byeon, S., Choi, J., & Hwang, I. (2023) · IEEE Open Journal of Control Systems · doi:10.1109/ojcsys.2023.3340034 · Cited by 4
Stensrud, R., Mikkelsen, B., & Valaker, S. (2024) · Human-Intelligent Systems Integration · doi:10.1007/s42454-024-00050-y · Cited by 4
Sebastian, L., & Schulte, A. (2024) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms59971.2024.10555606 · Cited by 4
Future of Team Research: On Teamness and Dimensions of Variability in Teams
Cohen, M. C., Cooke, N. J., Gorman, J. C., Fiore, S. M., Gray, W. D., Banerjee, S., Hirshfield, L., & Nguyen, D. (2024) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813241278288 · Cited by 4
Human-AI Teaming in the Urban Air Mobility Coordinator Work Position: A Proof-of-Concept Design
Westin, C., Klang, K. J., Basjuka, J., Söderholm, G., Lundberg, J., Bång, M., Palmerius, K. L., Boonsong, S., Taraldsson, Å., & Fylkner, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93721-7_18 · Cited by 4
Chen, S., & Zhao, Y. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2530072 · Cited by 4
Hohm, A., Happel, O., Hurtienne, J., & Grundgeiger, T. (2021) · Mensch und Computer (MuC) · doi:10.1145/3473856.3474227 · Cited by 4
Team and Individual Trust Progression for Human-Autonomy Teaming in Next Generation Combat Vehicles
Graham, H. D., Cohen, M. C., Hrabovsky, J., Orth, D., & Cooke, N. J. (2022) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181322661054 · Cited by 4
Naik, S., Toombs, A. L., Snellinger, A., Saponas, S., & Hall, A. K. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2510.06224 · Cited by 4
Panchanadikar, R., & Freeman, G. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790869 · Cited by 3
Al‐Husseini, M., Wray, K. H., & Kochenderfer, M. J. (2024) · Journal of Aerospace Information Systems · doi:10.2514/1.i011368 · Cited by 3
Graf, B., Müller, R., Ellwart, T., & Antoni, C. H. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100727 · Cited by 3
Basappa, R., Lancaster, C., Mallick, R., Flathmann, C., & McNeese, N. J. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251361002 · Cited by 3
Self-citationAn Exploratory Study on AI-driven Visualisation Techniques on Decision Making in Extended Reality
Dong, Z., Han, B., Zhang, J., Wen, R., Ens, B. M., Clark, A., & Piumsomboon, T. (2024) · Australian Conference on Human-Computer Interaction (OzCHI) · doi:10.1145/3726986.3727036 · Cited by 3
Decision-Making Amid Information-Based Threats in Sociotechnical Systems: A Review
Allred, A. R., Richardson, E. E., Bostrom, S. R., Crum, J. E., II, Spencer, C. A., Tossell, C. C., Niemeyer, R. E., Hirshfield, L., & Anderson, A. P. (2026) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434261497467 · Cited by 3
Margetis, G., Ntoa, S., & Stephanidis, C. (2025) · Springer eBooks · doi:10.1007/978-981-95-3658-0_71 · Cited by 3
Pinto, A. H. M., Lounis, C., Causse, M., & Chanel, C. P. C. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2575895 · Cited by 3
Metadata
- Authors
- O’Neill, T., McNeese, N., Barron, A., & Schelble, B.
- Affiliations
- University of Calgary, Calgary, AB, Canada
- Clemson University (US)
- University of Waterloo (CA)
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2020-10-22
- Volume
- 64
- Issue
- 5
- Pages
- 904-938
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- PMC
- PMC9284085
- OpenAlex
- W3093840653
- Cited by
- 695
- References
- 155
- Citations by year
- 2026: 225 · 2025: 174 · 2024: 120 · 2023: 88 · 2022: 63 · 2021: 23 · 2020: 2
- FWCI
- 8.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Human Factors The Journal of the Human Factors and Ergonomics Society (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- TigerPrints (Clemson University) (submittedVersion)
- Sage Journals Data (cc-by)
- Funders
- Social Sciences and Humanities Research Council of Canada
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- human-autonomy teaming, autonomous agents, team composition, task characteristics, individual differences, team training, team communication, team performance, human agency
- MeSH terms
- Communication, Humans
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 ›
Are you an author, or do you see a problem with this page? Request a review
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.
