# Who/What is My Teammate? Team Composition Considerations in Human-AI Teaming

arXiv: https://arxiv.org/abs/2105.11000 · HTML: https://mlsanity.com/paper/2105.11000/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 72 references that are in the map.
- First posted on arXiv 23 May 2021.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Learning with Opponent-Learning Awareness](https://mlsanity.com/paper/1709.04326/) (2017)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges](https://mlsanity.com/paper/1808.06211/) (2018)
- [Effects of Voice-Based Synthetic Assistant on Performance of Emergency Care Provider in Training](https://mlsanity.com/paper/2008.05064/) (2020)

## Built on by (most cited first, 12 of 12)

- [AI-enhanced Collective Intelligence](https://mlsanity.com/paper/2403.10433/) (2024)
- [A Survey on Human-AI Collaboration with Large Foundation Models](https://mlsanity.com/paper/2403.04931/) (2024)
- [Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming](https://mlsanity.com/paper/2308.16785/) (2023)
- [Scalable Interactive Machine Learning for Future Command and Control](https://mlsanity.com/paper/2402.06501/) (2024)
- [To Use or to Refuse? Re-Centering Student Agency with Generative AI in Engineering Design Education](https://mlsanity.com/paper/2510.19342/) (2025)
- [What Types of Human-AI Teams Exist?](https://mlsanity.com/paper/2607.02198/) (2026)
- [Expansion of situations theory for exploring shared awareness in human-intelligent autonomous systems](https://mlsanity.com/paper/2406.04956/) (2024)
- [Controllable Complementarity: Subjective Preferences in Human-AI Collaboration](https://mlsanity.com/paper/2503.05455/) (2025)
- [Advancing Human-Machine Teaming: Concepts, Challenges, and Applications](https://mlsanity.com/paper/2503.16518/) (2025)
- [Understanding Role Switching in Human-AI Collaboration through Multimodal Behavioral Signals](https://mlsanity.com/paper/2509.20666/) (2025)
- [Bounded Normative Equivalence in Human-AI Cooperation: Group Behaviour, Not Partner Labels, Predicts Cooperation under Anonymous Aggregate Feedback](https://mlsanity.com/paper/2601.20487/) (2026)
- [Measuring Successful Cooperation in Human-AI Teamwork: Development and Validation of the Perceived Cooperativity and Teaming Perception Scales](https://mlsanity.com/paper/2604.24461/) (2026)

## Abstract (arXiv)

There are many unknowns regarding the characteristics and dynamics of human-AI teams, including a lack of understanding of how certain human-human teaming concepts may or may not apply to human-AI teams and how this composition affects team performance. This paper outlines an experimental research study that investigates essential aspects of human-AI teaming such as team performance, team situation awareness, and perceived team cognition in various mixed composition teams (human-only, human-human-AI, human-AI-AI, and AI-only) through a simulated emergency response management scenario. Results indicate dichotomous outcomes regarding perceived team cognition and performance metrics, as perceived team cognition was not predictive of performance. Performance metrics like team situational awareness and team score showed that teams composed of all human participants performed at a lower level than mixed human-AI teams, with the AI-only teams attaining the highest performance. Perceived team cognition was highest in human-only teams, with mixed composition teams reporting perceived team cognition 58% below the all-human teams. These results inform future mixed teams of the potential performance gains in utilizing mixed teams' over human-only teams in certain applications, while also highlighting mixed teams' adverse effects on perceived team cognition.