Ergonomics

Beyond Compliance and Control: Human Trust, Mental Models, and Cognitive Adaptation from Automated to AI-Augmented Work

Background and Motivation

The relationship between human operators and automated systems has been a defining concern of ergonomics since Bainbridge’s (1983) seminal account of the ironies of automation. For decades, the field has framed this relationship in terms of compliance and control: how well operators monitor, understand, and override machines that were designed to support them. Measures of mental workload, situation awareness, and trust in automation have become cornerstones of cognitive ergonomics precisely because they capture the costs and failures of this relationship.

Automation, however, is no longer a fixed set of rules executing predefined instructions. Artificial intelligence is transforming work systems into adaptive, context-sensitive agents that learn, infer, and act with varying degrees of autonomy. This shift — from automated systems that follow human-specified logic to AI-augmented systems that generate behaviour not fully predictable for the human — poses fundamentally new challenges for human factors and ergonomics. The classical compliance-and-control framing is no longer sufficient. Operators are no longer simply controlling machines; they are working alongside and in teams with agents whose internal states are opaque, whose boundaries of competence are difficult to specify, and whose role in joint decision-making is still being negotiated.

This special issue of Ergonomics addresses that transition directly. We ask: how do human trust, mental models, and cognitive adaptation need to be reconceptualised as automation gradually or distruptingly evolves into AI-augmented work? The issue brings together theoretical, empirical, and methodical contributions that examine the changing sociotechnical work system from conventional automated systems to AI-assisted teaming — looking at different domains like road transport, industrial manufacturing, human-robot interaction, and cultural sociotechnical systems.

Scope and Aims

This special issue invites contributions that advance understanding of the ergonomics of human interaction with automated and AI-augmented systems. We are particularly interested in work that:

• examines how operators form, update, and (mis-)calibrate trust in automation and AI systems, including theoretical frameworks for trust calibration and empirical studies of reliance and disuse;
• investigates mental model development, accuracy, and updating in the context of partially automated, highly automated, and AI-augmented systems;
• studies cognitive adaptation as system autonomy increases, including changes in workload, situation awareness, decision-making, and out-of-the-loop performance;
• addresses the design and evaluation of interfaces that support appropriate trust, transparent system behaviour, and effective human–AI teaming;
• explores human–AI and human–robot teaming, including team composition, role allocation, social cognition, and the evolution from interaction to collaborative teaming;
• investigates technology acceptance and user experience across levels of automation, including across user populations and cultural contexts;
• examines information inquiry, communication, and decision-making in human-only versus human–AI teams in applied settings;
• considers cross-cutting issues of function allocation and the transition between manual, automated, and AI-augmented modes of work.

Authors who are uncertain whether their topic falls within the scope of this special issue are welcome to contact Eva Gößwein, directly at [email protected]. A brief description of the planned contribution is sufficient, and a timely assessment of fit will be provided.