Goal: Although automated social skills training has been proposed to enhance human social skills, the following two aspects have not been adequately explored: what types of feedback are effective from virtual agents and the extent to which such systems enhance users' social self-efficacy. Methods: We developed an automated social skills trainer+ that follows human-based social skills training processes and implemented two types of feedback: 1) a summary of the displayed feedback and 2) feedback based on the results of their previous training. Using our developed system, we measured social self-efficacy, feedback evaluations, and the third-party ratings of participants between pre- and post-training as well as their social responsiveness scales. Results: Self-efficacy is significantly correlated to the social responsiveness scale (r = -0.72) and can be improved with our system (mean improvement of 0.68, p < 0.05). The participants highly rated the feedback that was compared to their past training (14 out of 16, p < 0.05) more than the cases without it and the displayed summary feedback (11 out of 16, p = 0.21) more than the verbal comments. Conclusions: Our system effectively summarized user feedback in terms of user self-efficacy and third-party ratings.
Keywords: Embodied conversational agents; self-efficacy; social skills training; summary feedback.