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Cognitive Readiness for Human-AI Collaboration
Sébastien Tremblay1, Delphine de Hemptinne1, Gabrielle Teyssier-Roberge2
1School of Psychology, Université Laval, Quebec City, QC, Canada.
Human Factors
|August 3, 2026
Summary
Human-AI collaboration requires mutual readiness, focusing on cognitive, metacognitive, and team skills for both humans and AI. Addressing barriers like trust miscalibration is key for effective human-agent teaming (HAT).
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence
- Cognitive Science
Background:
- AI integration into workplaces necessitates understanding human-agent teaming (HAT).
- Humans face challenges like maintaining situation awareness and calibrating trust with AI teammates.
- HAT readiness involves operational competencies and regulatory capacities.
Purpose of the Study:
- Examine cognitive, metacognitive, and team competency requirements for productive human-AI collaboration.
- Identify capabilities for AI as a competent collaborator.
- Determine human readiness for AI collaboration.
Main Methods:
- Structured narrative review of literature from 2010-2026.
- Searched multiple academic databases and used citation tracking.
- Synthesized findings from 192 selected articles.
Main Results:
- Communication inflexibility, limited shared understanding, and trust miscalibration are key HAT barriers.
- Trust calibration and metacognitive awareness are critical, yet under-operationalized, HAT readiness dimensions.
- AI systems may surpass human cognitive abilities, posing collaboration challenges.
Conclusions:
- Mutual readiness, involving metacognitive and adaptive capabilities for both humans and AI, is essential for HAT.
- Cross-training and co-learning methods show promise for enhancing shared understanding and collaboration.
- Principles for designing AI and preparing humans can improve HAT effectiveness, reliability, and resilience.
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