This working group aims to develop a research-based AI concept inventory (AI CI) to assess the understanding of foundational AI concepts among non-experts. By identifying core concepts and common misconceptions through literature reviews, expert consultations, and iterative validation, the group will create a user-friendly assessment tool that can be used to capture snapshots of AI understanding, support benchmarking across contexts, and inform educational initiatives and policy. Designed for diverse non-expert audiences, including educators, students, and the general public, this tool can provide valuable insights into how AI knowledge evolves over time, contributing to the broader goal of promoting AI literacy in everyday contexts.

Chatbots are becoming omnipresent in our daily lives. Despite rapid improvements in natural language processing in the last years, the technology behind chatbots is still not completely mature, and chatbots still make a lot of mistakes during their interactions with users. Since it is not possible to completely prevent mistakes due to technological constraints, this article aims to investigate whether a human-like communication style can reduce the negative impact of chatbots’ mistakes on users. Taking a combination of the Technology Acceptance Model and the concepts of Perceived Enjoyment and Social Presence as a theoretical basis, we conducted an online experiment in which participants interacted with a chatbot and completed a survey afterwards. We found that chatbot mistakes have a negative effect on users’ perceptions of Ease of Use, Usefulness, Enjoyment, and Social Presence. Human-like communication was found to be effective in reducing the negative impact of mistakes on Perceived Enjoyment. Theoretical and practical implications are discussed.

Robots are becoming an integral part of society, and might soon take on roles involving making morally relevant decisions. In a pre-registered experiment (n = 184), we investigated which factors modulate the extent to which we trust a robot to make a moral choice. Specifically, the effects of anthropomorphic appearance and anthropomorphic agency and affect attributions were assessed. Participants were presented with moral dilemmas in which the individual having to make a decision was a humanoid or mechanical robot. Each robot was described in vignettes in which they were attributed with agency and/or affective states. Subsequently, participants’ implicit moral trust in the robot was measured, as well as explicit trust, perceived capability of the robot, and the extent to which they felt the robot was responsible for its choice. Both agency and affective state attributions were found to impact participants’ implicit and explicit trust as well as the perceived capability of the robot. Moreover, across conditions, mechanical robots were trusted significantly more than humanoid robots to take moral choices.