The role of trust within Human-Computer Interaction is being redefined. With the increasing omnipresence, autonomy, and opacity of technology, users often struggle to understand the capabilities and limitations of systems. In this article, we present the results of an empirical study designed to provide a practical, evidence-based interpretation of trust propensity assessment using the Human-Computer Trust Scale (HCTS). We outline the process used to develop a guideline for interpreting the instrument’s results and explain the rationale for our decisions, advocating for calibrating trust in technology within HCI. Our findings demonstrate that the HCTS is a promising tool for conducting an initial evaluation of propensity to trust, but that such an assessment requires reflection and interpretation that should be considered within the context of the interaction.

User trust in Artificial Intelligence (AI) enabled systems has been increasingly recognized and proven as a key element to fostering adoption.It has been suggested that AI-enabled systems must go beyond technical-centric approaches and towards embracing a more human-centric approach, a core principle of the human-computer interaction (HCI) field.This review aims to provide an overview of the user trust definitions, influencing factors, and measurement methods from 23 empirical studies to gather insight for future technical and design strategies, research, and initiatives to calibrate the user-AI relationship.The findings confirm that there is more than one way to define trust.Selecting the most appropriate trust definition to depict user trust in a specific context should be the focus instead of comparing definitions.User trust in AI-enabled systems is found to be influenced by three main themes, namely socio-ethical considerations, technical and design features, and user characteristics.User characteristics dominate the findings, reinforcing the importance of user involvement from development through to monitoring of AI-enabled systems.Different contexts and various characteristics of both the users and the systems are also found to influence user trust, highlighting the importance of selecting and tailoring features of the system according to the targeted user group's characteristics.Importantly, socio-ethical considerations can pave the way in making sure that the environment where user-AI interactions happen is sufficiently conducive to establish and maintain a trusted relationship.In measuring user trust, surveys are found to be the most common method followed by interviews and focus groups.In conclusion, user trust needs to be addressed directly in every context where AI-enabled systems are being used or discussed.In addition, calibrating the user-AI relationship requires finding the optimal balance that works for not only the user but also the system.