Mental models represent the internal frameworks people construct to make sense of how systems work. With traditional interfaces, users develop relatively stable mental models through consistent design patterns. Buttons perform actions, menus contain options, and windows organize content. AI systems, however, present a fundamentally different challenge for users' cognitive frameworks. Unlike rule-based interfaces with predictable behaviors, AI-powered systems learn, adapt, and sometimes produce unexpected outputs. Users struggle to form accurate mental models when facing interfaces that appear to make decisions autonomously, offer different responses to the same input, or demonstrate capabilities that seem to change over time.

Mental models matter significantly more in AI contexts precisely because they determine not just usability but appropriate trust. When users overestimate AI capabilities, disappointment follows. When they underestimate capabilities, valuable functionality goes unused. Most critically, inaccurate mental models lead to fundamental misunderstandings about what the system is doing with user data, why it makes certain recommendations, and where human oversight exists in the process.