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

Most. Sharmin Sultana Samu, Nafisa Khan, Kazi Toufique Elahi, Tasnuva Binte Rahman, Md. Rakibul Islam, Farig Sadeque

TL;DR

The paper investigates whether AI decision-support acts as a teammate or a tool by examining interaction design, trust calibration, collaboration frameworks, and high-stakes healthcare applications. It analyzes literature from 2023–2025 to reveal that static interfaces and miscalibrated trust limit efficacy, with explainability alone often increasing cognitive burden and passive behavior. It argues for adaptive, context-aware interfaces that support shared mental models and negotiated authority, moving beyond explainability-centric designs toward true team-based collaboration. The work provides a roadmap for longitudinal, adaptive evaluations and ethically grounded designs to realize robust, responsible human–AI collaboration in decision-making, particularly in healthcare.

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.

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

TL;DR

The paper investigates whether AI decision-support acts as a teammate or a tool by examining interaction design, trust calibration, collaboration frameworks, and high-stakes healthcare applications. It analyzes literature from 2023–2025 to reveal that static interfaces and miscalibrated trust limit efficacy, with explainability alone often increasing cognitive burden and passive behavior. It argues for adaptive, context-aware interfaces that support shared mental models and negotiated authority, moving beyond explainability-centric designs toward true team-based collaboration. The work provides a roadmap for longitudinal, adaptive evaluations and ethically grounded designs to realize robust, responsible human–AI collaboration in decision-making, particularly in healthcare.

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.
Paper Structure (34 sections, 1 figure, 1 table)

This paper contains 34 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Human–AI Interaction Design & Interface Patterns
  3. Explanation and Transparency Interfaces
  4. Trust Calibration and Cognitive Load Management
  5. Interactive and Collaborative Interface Patterns
  6. Ethical and Context-Sensitive Interaction Design
  7. Dataset Design for Human–AI Interaction Studies
  8. Evaluation Metrics for Human–AI Interaction
  9. Research Gaps and Future Directions
  10. Trust, Explainability and Human Confidence Calibration
  11. Human Confidence and Reliance Calibration
  12. Explainability, Trust and Reliance Behavior
  13. Selective Trust and Context Sensitivity
  14. Human Expertise, Mental Models and Complementarity
  15. Datasets for Studying Human–AI Decision-Making and Confidence
  16. ...and 19 more sections

Figures (1)

  • Figure 1: Taxonomy of 'AI as Teammate or Tool?'.