Human-Centered Intelligent Systems: Integrating AI, Neuroscience, Psychology, and Engineering

Artificial Intelligence has made remarkable progress in learning, perception, and automation, yet many systems still differ significantly from human intelligence in flexibility, reasoning, and contextual understanding. Meanwhile, neuroscience and cognitive psychology have generated deep knowledge about how humans perceive, learn, remember, attend, and make decisions. Recent years have seen increasing interest in combining these domains through neuro-inspired AI, cognitive modeling, brain-computer interfaces, and human-centered interaction design. At the same time, engineering advances in IoT, cloud computing, and edge intelligence now allow intelligent systems to operate continuously in real-world environments using distributed sensing and computation. These developments create a unique opportunity to merge biological understanding with computational power, enabling the next generation of adaptive, explainable, and scalable intelligent systems. Beyond technological advances, increasing attention is being devoted to explainable AI, trustworthy autonomous systems, and ethical human-machine interaction. These challenges require a deeper integration of neuroscience, psychology, and engineering perspectives to ensure that intelligent systems remain interpretable, reliable, and socially acceptable.The goal of this Research Topic is to address the growing need for intelligent systems that are not only technically advanced but also grounded in human cognition, behavior, and neurobiological principles. Current AI systems often achieve strong performance but may lack explainability, adaptability, and alignment with human users in dynamic real-world contexts. To overcome these limitations, our main idea is to promotes interdisciplinary collaboration among researchers in AI, neuroscience, psychology, HCI, and engineering. The Research Topic specifically aims to foster methodological and conceptual integration across these disciplines, combining neuroscientific evidence, psychological models of cognition and behavior, AI algorithms, and engineering methodologies to design intelligent systems that are adaptive, explainable, trustworthy, and aligned with human needs. By integrating cognitive models, neural mechanisms, and human-centered design principles into computational systems, new technologies can become more robust, transparent, and effective. IoT, cloud, and edge technologies are considered enabling infrastructures that support the deployment, scalability, and real-time operation of human-centered intelligent systems rather than standalone research domains. Through shared methods and cross-disciplinary dialogue, this initiative aims to accelerate innovation in healthcare, education, rehabilitation, smart environments, and human-machine collaboration.This Research Topic welcomes theoretical, methodological, experimental, and application-oriented contributions that bridge AI, neuroscience, psychology, and engineering, particularly those demonstrating meaningful integration across these disciplines rather than isolated advances within any single field. Authors are encouraged to highlight how biological, cognitive, behavioral, and computational perspectives can jointly contribute to the development of human-centered intelligent systems. Topics of interest include:• Cognitive models of perception, attention, memory, and decision-making• Neural mechanisms of behavior• Neuroengineering and brain signal processing• Brain-computer interfaces• Neuro-inspired machine learning• HCI and cognitive ergonomics• Human-robot interaction• Embodied cognition• Behavioral modeling• Intelligent healthcare systems• Adaptive educational technologies• IoT-enabled smart environments• Cloud-edge architectures• Federated learning• Distributed intelligent systems• Explainable AI (XAI)• Trustworthy AI• Ethical AI• Responsible AI• Human-AI collaboration• Human-in-the-loop systems• Cognitive trust modeling • Fairness and transparency in intelligent systems. We invite Original Research, Review Articles, Methods papers, Mini Reviews, Perspectives, Case Studies, and interdisciplinary application papers. Submissions should clearly demonstrate scientific rigor and explicitly address interdisciplinary integration, explainability, trustworthiness, ethical implications, or practical relevance for human-centered intelligent systems. Topic editors: Cristian Randieri, Stergios Makris, Christian Napoli, Alfredo Milani Source: https://www.frontiersin.org/research-topics/81085/human-centered-intelligent-systems-integrating-ai-neuroscience-psychology-and-engineering