AI as Teammate or Tool? A Review of Human-AI Interaction in Decision Support
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911453359898624 |
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| author | Samu, Most. Sharmin Sultana Khan, Nafisa Elahi, Kazi Toufique Rahman, Tasnuva Binte Islam, Md. Rakibul Sadeque, Farig |
| author_facet | Samu, Most. Sharmin Sultana Khan, Nafisa Elahi, Kazi Toufique Rahman, Tasnuva Binte Islam, Md. Rakibul Sadeque, Farig |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15865 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI as Teammate or Tool? A Review of Human-AI Interaction in Decision Support Samu, Most. Sharmin Sultana Khan, Nafisa Elahi, Kazi Toufique Rahman, Tasnuva Binte Islam, Md. Rakibul Sadeque, Farig Human-Computer Interaction Artificial Intelligence Computation and Language 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. |
| title | AI as Teammate or Tool? A Review of Human-AI Interaction in Decision Support |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2602.15865 |