This chapter focuses on a transfer from research on trust processes in the interaction with automated vehicles to the interaction with current AI systems like Large Language Models (LLMs). After discussing parallels between the domains and transferable research insights, the psychological processes in trust calibration are discussed along the central propositions of the Three Stages of Trust framework (Kraus in Psychological processes in the formation and calibration of trust in automation, 2020). The framework emphasises the dynamic role of trust and the iterative learning about a system’s trustworthiness with presented information prior and during system interaction. Furthermore, it underlines the role of situational context and individual user differences. Designing AI systems that support calibrated trust involves transparent communication of capabilities and limitations, integrating human-centered design principles, and offering adaptive information to aid decision-making. These strategies aim to foster a balanced and informed use of AI, eventually fostering an efficient and safe interaction.