A Model of Integrated Information Processing in Human-AI Interaction

2026-06-05

AI Summary

This study addresses the absence of a unified theoretical framework that integrates psychological mechanisms, task orientation, and human-centered design principles to explain how AI system design influences human behavior. To bridge this gap, the authors propose the Integrated Information Processing (IIP) model, grounded in cybernetics and action regulation theory, which conceptualizes human–AI interaction as coupled control loops and establishes a common information-processing language applicable to both agents. The model introduces three integrative quality metrics—input sufficiency, reference consistency, and output operability—to theoretically predict human-centered benchmarks such as transparency and controllability, while mapping interface design choices to anticipated user behaviors. This framework offers actionable guidance for designing human–AI collaboration across diverse scenarios, fostering interactions characterized by greater autonomy and synergy.

Abstract

For Human-AI Interaction (HAII) research to move forward, theoretical work linking psychological mechanisms to interface design is needed. Such work should extend rather than replace established HCI and automation research, adapting to the increasing autonomy and agency of AI systems. Building on prior frameworks focused on roles and levels in human interaction with automation, a gap remains from a psychological view: a task-centered, process-oriented account that links mechanisms of action regulation to concrete design and evaluation levers for human-AI coupling, expressed in a unified vocabulary for human and machine. Moreover, existing models may describe how a system is designed (e.g., function allocation in automation) but fall short in showing how this design affects human behavior. We present the Integrated Information Processing (IIP) model, a task-centered, cybernetic model that conceptualizes humans, machines, and their joint activity as coupled control loops. The IIP model uses a unified modeling language for human and artificial agents, making psychological models of action regulation accessible for AI system design. As a core feature, we argue that efficacy within a shared task is characterized by three integration qualities, input adequacy, reference consonance, and output operativity, which critically influence benchmarks of human-centeredness such as transparency and controllability. The model maps interface choices (e.g., XAI techniques) to theory-driven expectations of user behavior, guiding interface design and evaluation. To this end, we present (1) a continuity-preserving theoretical discourse that extends HAII to agency in AI; (2) the IIP model with three information-processing qualities; and (3) applications of the IIP model to exemplary use cases demonstrating implications for interface design.

Problem

• Human-AI Interaction
• Integrated Information Processing
• Action Regulation
• Interface Design
• Psychological Mechanisms

Innovation

• Integrated Information Processing
• human-AI interaction
• action regulation
• cybernetic model
• interface design