Mental model

A mental model is a user’s simplified understanding of how a system, process or situation works. In AI use, mental models shape what people expect from a system: memory, knowledge, certainty, intention, creativity or reliability.

Mental models are not only individual mistakes. They are shaped by interface design, product language, marketing, repeated use and public metaphors about AI.

Why it matters

People act on the model they have in mind, not on the system architecture itself. If a user imagines AI as a search engine, a database, a person or an assistant, they will ask different questions and verify outputs differently.

A poor mental model can lead to overtrust, underuse, wrong delegation or confusion about what the model output actually represents.

Human-AI angle

Mental models connect technical behavior with human expectation. They help explain why the same system can feel reliable to one person and risky to another.

In organizations, the shared mental model matters too. Teams need a practical language for what AI can support, what it cannot own and where human judgment remains necessary.

Related concepts

• AI literacy
• Calibrated trust
• Anthropomorphism
• Context window
• Model output

Sources and context

• Gentner, D., & Stevens, A. L. (Eds.). (1983). Mental models. Lawrence Erlbaum Associates. https://archive.org/details/mentalmodels00theo
• Norman, D. A. (1983). Some observations on mental models. In D. Gentner & A. L. Stevens (Eds.), Mental models (pp. 7-14). Lawrence Erlbaum Associates. https://www.taylorfrancis.com/chapters/edit/10.4324/9781315802725-2/observations-mental-models-donald-norman
• Staggers, N., & Norcio, A. F. (1993). Mental models: Concepts for human-computer interaction research. International Journal of Man-Machine Studies, 38(4), 587-605. https://doi.org/10.1006/imms.1993.1028

Related reading

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