# AI as Teammate or Tool? A Review of Human-AI Interaction in Decision Support

arXiv: https://arxiv.org/abs/2602.15865 · HTML: https://mlsanity.com/paper/2602.15865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making](https://mlsanity.com/paper/2208.07960/) (2022)
- [A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective](https://mlsanity.com/paper/2304.08795/) (2023)
- ["Are You Really Sure?" Understanding the Effects of Human Self-Confidence Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2403.09552/) (2024)
- [Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant](https://mlsanity.com/paper/2501.17546/) (2025)
- [Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making](https://mlsanity.com/paper/2302.06503/) (2023)
- [A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams](https://mlsanity.com/paper/2401.14432/) (2024)
- [Are We Asking the Right Questions?: Designing for Community Stakeholders' Interactions with AI in Policing](https://mlsanity.com/paper/2402.05348/) (2024)
- [Engaging with AI: How Interface Design Shapes Human-AI Collaboration in High-Stakes Decision-Making](https://mlsanity.com/paper/2501.16627/) (2025)
- [The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted Decision-Making](https://mlsanity.com/paper/2502.14311/) (2025)
- [Revisiting Trust in the Era of Generative AI: Factorial Structure and Latent Profiles](https://mlsanity.com/paper/2510.10199/) (2025)
- [Human-AI Collaboration and Explainability for 2D/3D Registration Quality Assurance](https://mlsanity.com/paper/2507.17597/) (2025)

## Built on by (most cited first, 1 of 1)

- [Working with Agentic `Teammates': When a New Organizational Actor Collides with the Human Ecosystem of Work](https://mlsanity.com/paper/2609.29901/) (2026)

## Abstract (arXiv)

The integration of Artificial Intelligence (AI) necessitates determining whether systems function as tools or collaborative teammates. In this study, by synthesizing Human-AI Interaction (HAI) literature, we analyze this distinction across four dimensions: interaction design, trust calibration, collaborative frameworks and healthcare applications. Our analysis reveals that static interfaces and miscalibrated trust limit AI efficacy. Performance hinges on aligning transparency with cognitive workflows, yet a fluency trap often inflates trust without improving decision-making. Consequently, an overemphasis on explainability leaves systems largely passive. Our findings show that current AI systems remain largely passive due to an overreliance on explainability-centric designs and that transitioning AI to an active teammate requires adaptive, context-aware interactions that support shared mental models and the dynamic negotiation of authority between humans and AI.