Adapt and overcome: Perceptions of adaptive autonomous agents for human-AI teaming
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AI Agents have the ability to function with minimal or no human intervention after deployment.
“Autonomy: A central feature of AI Agents is their ability to function with minimal or no human intervention after deployment [65].”
From AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges (2025) · cites this paper as [65]
Cites
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 · see citing works
Ten challenges for making automation a ‘team player’ in joint human-agent activity
Klein, G., Woods, D. D., Bradshaw, J. M., Hoffman, R. R., & Feltovich, P. J. (2004) · IEEE Intelligent Systems · doi:10.1109/mis.2004.74 · Cited by 590 · see citing works
Cooke, N. J., Gorman, J. C., Myers, C. W., & Duran, J. L. (2013) · Cognitive Science · doi:10.1111/cogs.12009 · Cited by 522 · see citing works
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
Wang, D., Weisz, J. D., Muller, M., Ram, P., Geyer, W., Dugan, C., Tausczik, Y., Samulowitz, H., & Gray, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359313 · Cited by 342 · see citing works
adaptive aiding for human/computer control
Rouse, W. B. (1988) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/001872088803000405 · Cited by 252 · see citing works
Does Explainable Artificial Intelligence Improve Human Decision-Making?
Alufaisan, Y., Marusich, L. R., Bakdash, J. Z., Zhou, Y., & Kantarcioglu, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16819 · Cited by 106 · see citing works
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Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108097 · Cited by 301 · see citing works
AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103599 · Cited by 244 · see citing works
“• Autonomy: A central feature of AI Agents is their ability to function with minimal or no human intervention after deployment [63].”
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025) · SuperIntelligence - Robotics - Safety & Alignment · doi:10.70777/si.v2i3.15161 · Cited by 186 · see citing works
From testbeds to high-stakes work: a review of human-AI teaming domains and teaming factors
Kargarnovin, S., Hernandez, C. I., Reiners, D., Cruz-Neira, C., Bochenek, G., & Karwowski, W. (2026) · Frontiers in Robotics and AI · doi:10.3389/frobt.2026.1733942 · Cited by 2 · see citing works
- Authors
- Hauptman, A. I., Schelble, B. G., McNeese, N. J., & Madathil, K. C.
- Affiliations
- Clemson University (US)
- Corresponding author
- Hauptman, Allyson I.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 138
- Pages
- 107451
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W4293216819
- Cited by
- 110
- References
- 6
- Citations by year
- 2026: 25 · 2025: 50 · 2024: 24 · 2023: 11
- FWCI
- 20.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers in Human Behavior (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › 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-AI teaming, working cycle, incident response, team performance, team cohesion, team dynamics
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.
Statistics and arithmetic are recomputed, not judged.
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