# Unraveling Human-AI Teaming: A Review and Outlook

arXiv: https://arxiv.org/abs/2504.05755 · HTML: https://mlsanity.com/paper/2504.05755/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 148 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 22 of 22)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Why Do Multi-Agent LLM Systems Fail?](https://mlsanity.com/paper/2503.13657/) (2025)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer](https://mlsanity.com/paper/1711.06664/) (2017)
- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [Agent AI: Surveying the Horizons of Multimodal Interaction](https://mlsanity.com/paper/2401.03568/) (2024)
- [Measuring AI Ability to Complete Long Software Tasks](https://mlsanity.com/paper/2503.14499/) (2025)
- [The Ethics of Advanced AI Assistants](https://mlsanity.com/paper/2404.16244/) (2024)
- [Best Practices and Lessons Learned on Synthetic Data](https://mlsanity.com/paper/2404.07503/) (2024)
- [LLMs Will Always Hallucinate, and We Need to Live With This](https://mlsanity.com/paper/2409.05746/) (2024)
- [A Survey on Robotics with Foundation Models: toward Embodied AI](https://mlsanity.com/paper/2402.02385/) (2024)
- [Regression Under Human Assistance](https://mlsanity.com/paper/1909.02963/) (2019)
- [A Taxonomy for Human-LLM Interaction Modes: An Initial Exploration](https://mlsanity.com/paper/2404.00405/) (2024)
- [Human-in-the-Loop Interpretability Prior](https://mlsanity.com/paper/1805.11571/) (2018)
- [Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis](https://mlsanity.com/paper/2309.12368/) (2023)
- [How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries](https://mlsanity.com/paper/2310.06778/) (2023)
- [The LLM Effect: Are Humans Truly Using LLMs, or Are They Being Influenced By Them Instead?](https://mlsanity.com/paper/2410.04699/) (2024)
- [Collaborating with AI Agents: Field Experiments on Teamwork, Productivity, and Performance](https://mlsanity.com/paper/2503.18238/) (2025)
- [Operationalising AI governance through ethics-based auditing: An industry case study](https://mlsanity.com/paper/2407.06232/) (2024)
- [Human-Machine Collaboration for Smart Decision Making: Current Trends and Future Opportunities](https://mlsanity.com/paper/2301.07766/) (2023)
- [Investigating the Role of Explainability and AI Literacy in User Compliance](https://mlsanity.com/paper/2406.12660/) (2024)

## Built on by (most cited first, 4 of 4)

- [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025)
- [What Types of Human-AI Teams Exist?](https://mlsanity.com/paper/2607.02198/) (2026)
- [From Retrieving Information to Reasoning with AI: Exploring Different Interaction Modalities to Support Human-AI Coordination in Clinical Decision-Making](https://mlsanity.com/paper/2601.22338/) (2026)
- [A study on human-agent teaming through spoken interaction: the impact of human individual traits and agent characteristics](https://mlsanity.com/paper/2610.04079/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) is advancing at an unprecedented pace, with clear potential to enhance decision-making and productivity. Yet, the collaborative decision-making process between humans and AI remains underdeveloped, often falling short of its transformative possibilities. This paper explores the evolution of AI agents from passive tools to active collaborators in human-AI teams, emphasizing their ability to learn, adapt, and operate autonomously in complex environments. This paradigm shifts challenges traditional team dynamics, requiring new interaction protocols, delegation strategies, and responsibility distribution frameworks. Drawing on Team Situation Awareness (SA) theory, we identify two critical gaps in current human-AI teaming research: the difficulty of aligning AI agents with human values and objectives, and the underutilization of AI's capabilities as genuine team members. Addressing these gaps, we propose a structured research outlook centered on four key aspects of human-AI teaming: formulation, coordination, maintenance, and training. Our framework highlights the importance of shared mental models, trust-building, conflict resolution, and skill adaptation for effective teaming. Furthermore, we discuss the unique challenges posed by varying team compositions, goals, and complexities. This paper provides a foundational agenda for future research and practical design of sustainable, high-performing human-AI teams.