Toward Human-Centered Human-AI Interaction: Advances in Theoretical Frameworks and Practice

2026-01-16

AI Summary

This work addresses the significant limitations of current centralized artificial intelligence systems in robustness, fairness, and interpretability, advocating for a human-centered human-AI collaboration paradigm. It pioneers a systematic interdisciplinary framework in China that integrates cognitive science, human factors engineering, and AI technologies to conceptualize human-AI interaction. The proposed framework encompasses joint human-AI cognitive systems, team situational awareness among intelligent agents, and shared social understanding, supported by a hierarchical Human-Centered AI (HCAI) architecture and a methodological taxonomy. Empirical studies in autonomous driving and intelligent cockpit scenarios demonstrate the effectiveness of the model in enhancing contextual awareness, trust calibration, and collaborative performance, thereby establishing both theoretical foundations and practical pathways toward trustworthy, cooperative human-centric intelligent systems.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

With the rapid development of artificial intelligence (AI), machines are increasingly evolving into intelligent agents, and the human-machine relationship is shifting from traditional"human-computer interaction"toward a new paradigm of"human-AI collaboration."However, technology-centered approaches to AI development have gradually revealed limitations such as fragility, bias, and low explainability, highlighting the urgent need for human-centered AI (HCAI) design philosophy. As a systems engineering approach, the successful implementation of HCAI depends critically on the design and optimization of high-quality human-AI interaction (HAII). This paper systematically reviews our research team's nearly decade-long exploration and practice in HCAI. At the level of research vision, we were among the first in China to systematically propose HAII as an interdisciplinary field and to develop a human-centered conceptual framework for human--AI collaboration. At the theoretical level, we introduced frameworks for human-AI joint cognitive systems, team-level situation awareness among intelligent agents, and shared social understanding, forming a relatively comprehensive theoretical system. At the methodological level, we established a hierarchical HCAI framework and a taxonomy of HCAI implementation methods. At the application level, we conducted a series of studies in domains such as autonomous driving, intelligent aircraft cockpit, and trust in human-AI collaboration, empirically validating the effectiveness of the proposed frameworks. Looking ahead, research on HCAI and HAII must continue to advance along three dimensions: theoretical deepening, methodological innovation, and application expansion, promoting the development of an intelligent society that is human-centered and characterized by harmonious human-AI coexistence.

Problem

• Human-Centered AI
• Human-AI Interaction
• AI Collaboration
• Explainability
• Bias

Innovation

• Human-Centered AI
• Human-AI Interaction
• Joint Cognitive Systems
• Situation Awareness
• Shared Social Understanding