Connected vehicles have become a promising platform for conversational agents. However, drivers might struggle to control systems that feature multiple AI agents. To improve the usability of increasingly complex systems and the user’s interaction experience, the integration of various conversational agents needs to be carefully considered. This study aims to investigate the following: What constitutes an efficient, user-friendly arrangement of the tasks performed by a home and a car artificial intelligence (AI) agent? What is the optimal method to trigger the use of such agents? A between-subjects factorial experiment was conducted with three types of AI agent setting (home AI generalist vs. vehicle AI generalist vs. home and vehicle AI specialists) and two activation methods (voice activation vs. push-to-talk activation). The results indicate that interacting with specialist AI agents enhances the perceived ease of use and the credibility of these agents. Furthermore, voice activation improves the social presence and attractiveness of an AI agent. The findings suggest that an AI agent that offers role specialization and a natural interaction method will improve drivers’ interaction experience.

Self-disclosure lowers one’s stress and anxiety and benefits their mental health. However, revealing one’s personal and unprotected information can be risky, and such a risk causes major practical difficulties in counseling services. People are reluctant to expose themselves because they are concerned about being negatively evaluated and judged by others, even professional counselors. Accordingly, this study designs and implements a fully automated text-based counseling chatbot that alleviates people’s self-disclosure burden. This chatbot is based on rapport-facilitating dialogue, which creates a mutually stable and favorable relationship between two interlocutors and eventually encourages people to self-disclose. Using 303 nonclinical samples, this study examines how rapport is activated between a user and the counseling chatbot and confirms whether it lowers psychological barriers to being judged and encourages self-disclosure. The findings offer practical implications for artificial intelligence (AI) psychotherapy service developers to design a counseling program to encourage people to reveal their mental issues without hesitation.

The present study investigates the effects of a chatbot’s motivation support style on the learner’s experience and intention to continue the study in the context of online English lectures. Seventy-nine undergraduate students were recruited from a large private university in Seoul, South Korea, and assigned to one of three learning plan development groups: develop a plan alone, autonomy support (i.e., a chatbot stimulating intrinsic motivation), or control support (i.e., a chatbot promoting extrinsic motivation) groups. The learners were classified into two groups based on their learning motivation types (i.e., intrinsic and extrinsic), and by doing so, the present study created a chatbot’s matched and non-matched motivation support conditions in learning plan development. The two support strategies were compared with a control condition (i.e., learners’ own plan making), and the results suggest that a chatbot with a non-matched motivation strategy increases learner self-efficacy, enjoyment, and intention to continue using the lecture. Furthermore, the study also explores the moderation effect of learning motivation types, and reveals that a chatbot’s control support significantly improves the learning experience. The present study provides new insight into improving user evaluation by strategically differentiating a chatbot’s conversational style and a user’s characteristics.

Although autonomous vehicles are increasingly becoming a reality, eliminating human intervention from driving may imply significant safety and trust-related concerns. To address this issue from a psychological perspective, this study applies layers of anthropomorphic cues to an artificial driving agent and explicates the process in which these cues promote positive evaluations and perceptions of an unmanned driving system. In a between-subjects factorial experiment (N = 89) consisting of three unmanned driving scenarios, participants interacted with an artificial driving agent with different levels of anthropomorphic cues induced by the variations in appearance (human-like vs. gadget-like) and autonomy (high vs. low) of the agent. The results indicated that human-like appearance and high autonomy were more effective in eliciting positive perceptions of the agent. In addition, a mediation analysis revealed that the greater level of anthropomorphism induced by human-like appearance and high autonomy in the agent evoked the feelings of social presence, which in turn positively affected the perceived intelligence and safety of and trust in the agent, suggesting that the extent to which users perceive the driving agent as intelligent, safe, and trustworthy is largely determined by the feelings of social presence experienced during their interaction.