The growing integration of artificial intelligence into educational, professional, and creative contexts is transforming fundamental aspects of human cognition and problem-solving. As AI systems become collaborative partners in tasks such as planning, coding, analysis, and reasoning, there are emerging questions about how individuals conceptualize problems, seek information, generate solutions, and reflect on their own thinking. Recent research in cognitive science and human–computer interaction has begun to reveal nuanced shifts in cognitive and metacognitive strategies, alongside new forms of human–AI socio-cognitive dynamics. Despite notable advances, the field still grapples with understanding the boundaries of uniquely human cognition, the risks of cognitive biases, and how AI can both scaffold and potentially undermine core reasoning capacities.

This Research Topic aims to advance both theoretical and empirical understanding of how human cognition adapts in the context of AI-mediated problem solving. The objective is to uncover not just how cognitive strategies evolve, but also the implications for creativity, accountability, and skill development. By inviting cross-disciplinary contributions, we seek to address important questions, including: how do metacognitive and problem-framing approaches change with AI participation; what are the impacts on outcomes such as solution quality and transfer; and what forms of bias or over-reliance may emerge? Furthermore, we hope to inspire actionable insights for the design of interfaces, training, and pedagogical interventions that foster human agency and deep learning in collaboration with intelligent systems.

The boundaries of this Research Topic are defined by investigations into cognition, metacognition, and learning as they relate explicitly to problem solving in the presence of AI systems. We are particularly interested in research that bridges laboratory studies and real-world applications, without being limited to any single domain or methodology. To gather further insights in these areas, we welcome articles addressing, but not limited to, the following themes:

- Evolution of cognitive and metacognitive strategies in AI-mediated problem solving
- The role of AI in shaping solution quality, creativity, abstraction, and critical thinking
- Emergence of cognitive biases, such as automation bias or reliance traps, in human–AI interaction
- Design of interfaces, prompts, and training for effective human–AI collaboration
- Theoretical and methodological advances in studying human cognition with AI support
- Applications spanning education, professional settings, scientific discovery, and creative work

Appendix: The following article types are welcome—empirical studies, theoretical reviews, methodological papers, design case studies, and translational research bridging theory and practice.

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Cognitive Processes in AI-Mediated Problem Solving