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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. 

Children’s use of computing devices has increased over the past 15 years, requiring age-appropriate user authentication systems. This paper details a research study which investigates continuous authentication systems that do not require user-initiated interactions as an accessible authentication model for not only children users, but users across different age groups, with specific application on personal computing devices.