The role of affect has been long studied in human–computer interactions. Unlike previous studies that focused on seven basic emotions, an avatar named Diana was introduced who expresses a higher level of emotional intelligence. To adapt to the users various affects during interaction, Diana simulates emotions with dynamic facial expressions. When two people collaborated to build blocks, their affects were recognized and labeled using the Affdex SDK and a descriptive analysis was provided. When participants turned to collaborate with Diana, their subjective responses were collected and the length of completion was recorded. Three modes of Diana were involved: a flat-faced Diana, a Diana that used mimicry facial expressions, and a Diana that used emotionally responsive facial expressions. Twenty-one responses were collected through a five-point Likert scale questionnaire and the NASA TLX. Results from questionnaires were not statistically different. However, the emotionally responsive Diana obtained more positive responses, and people spent the longest time with the mimicry Diana. In post-study comments, most participants perceived facial expressions on Diana’s face as natural, four mentioned uncomfortable feelings caused by the Uncanny Valley effect.

Motivational agents are virtual agents that seek to motivate users by providing feedback and guidance. Prior work has shown how certain factors of an agent, such as the type of feedback given or the agent's appearance, can influence user motivation when completing tasks. However, it is not known how nonverbal mirroring affects an agent's ability to motivate users. Specifically, would an agent that mirrors be more motivating than an agent that does not? Would an agent trained on real human behaviors be better? We conducted a within-subjects study asking 30 participants to play a "find-the-hidden-object" game while interacting with a motivational agent that would provide hints and feedback on the user's performance. We created three agents: a Control agent that did not respond to the user's movements, a simple Mimic agent that mirrored the user's movements on a delay, and a Complex agent that used a machine-learned behavior model. We asked participants to complete a questionnaire asking them to rate their levels of motivation and perceptions of the agent and its feedback. Our results showed that the Mimic agent was more motivating than the Control agent and more helpful than the Complex agent. We also found that when participants became aware of the mimicking behavior, it can feel weird or creepy; therefore, it is important to consider the detection of mimicry when designing virtual agents.

Continuous authentication (CA), a user authentication approach that continuously verifies a person’s identity without requiring explicit input, is increasingly being deployed in smart homes to maintain security posture throughout user sessions. However, prior research has overlooked user attitudes toward the increased data collection and surveillance associated with CA in smart homes. To bridge this gap, we conducted a focus group study with 33 participants, using a design probe video to simulate various CA implementation scenarios in smart homes. We explored participants’ current authentication methods (e.g., passwords and physiological biometrics) and examined their perceptions of CA. Through affinity diagramming, we found that participants perceive smart-home CA as presenting privacy and security challenges yet possessing great potential for enhanced usability. Participants also envisioned CA systems that offer more granular permission controls over personal data. Our findings indicate the contextual dependencies in balancing usability with privacy and security concerns. Our contributions include a comprehensive empirical dataset featuring the design probe video, participant transcripts, and a conceptual model of users’ nuanced understanding. We provide design recommendations for smart-home CA systems, emphasizing transparency as a crucial factor in building user trust and improving adoption rates.