Metadata about: For the training of interpersonal skills, such as those required in the medical field, virtual agents can provide a safe environment for practice. However, many agent systems are not developed with the ability to understand non-verbal input. Being able to automatically parse such input is essential for the practice of interpersonal skills such as empathy. Currently, it is still an open question which prosodic or visual features would aid automatic classification of empathy and how this knowledge can be used to support the practice of these skills. As a first step towards this goal, we report on 42 second-year nursing students practicing their empathy skills with a virtual patient or through collaborative role playing. We found that across both the role playing and simulation, students assessed their empathy as increasing over time but as higher during the role playing. This work contributes to the continued development of virtual agents for the training of interpersonal skills.

About: For the training of interpersonal skills, such as those required in the medical field, virtual agents can provide a safe environment for practice. However, many agent systems are not developed with the ability to understand non-verbal input. Being able to automatically parse such input is essential for the practice of interpersonal skills such as empathy. Currently, it is still an open question which prosodic or visual features would aid automatic classification of empathy and how this knowledge can be used to support the practice of these skills. As a first step towards this goal, we report on 42 second-year nursing students practicing their empathy skills with a virtual patient or through collaborative role playing. We found that across both the role playing and simulation, students assessed their empathy as increasing over time but as higher during the role playing. This work contributes to the continued development of virtual agents for the training of interpersonal skills.

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