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

TL;DR

This paper reviews how human-AI interaction design influences AI's role as a tool or teammate, emphasizing the need for adaptive, context-aware systems to improve decision support in healthcare and beyond.

Contribution

It synthesizes HAI literature to identify key dimensions affecting AI's role, highlighting the importance of adaptive interaction design for effective collaboration.

Findings

Static interfaces limit AI effectiveness.

Miscalibrated trust reduces decision quality.

Passive AI systems dominate due to explainability focus.

Abstract

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.