Conceptual model

Related papers: 20

About

A conceptual model in robotics and AI is an abstract, structured representation that captures the key variables, relationships, and mechanisms governing a system or phenomenon — without necessarily implementing them in code or hardware. These models serve as theoretical blueprints, drawing on established frameworks such as the Technology Acceptance Model (TAM), socio-technical systems theory, or trust models to explain how humans, robots, and organizations interact and influence one another. In robotics and AI, conceptual models are used to guide system design, predict user adoption behavior, map human-robot trust dynamics, and evaluate deployment strategies across domains including manufacturing, hospitality, logistics, and healthcare. Researchers construct these models by synthesizing prior literature, identifying relevant factors — such as perceived usefulness, safety concerns, or rapport — and proposing testable hypotheses about their relationships. Their importance lies in bridging theory and practice: before building or deploying a robotic system, conceptual models help engineers and policymakers anticipate how users will respond, what risks may arise, and which design choices matter most. They provide a shared language for interdisciplinary teams and form the foundation for empirical validation, ultimately accelerating the responsible and effective integration of intelligent systems into real-world environments.

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