Training applications which use responsive virtual-human technology (RVHT) – training tools based on sophisticated voice recognition and behavior modeling technologies -- have great potential for improving training of interaction skills essential to effective interviewing, such as refusal avoidance, probing, addressing questions related to informed consent, and the like. However, our understanding of how to model the behavior of responsive virtual humans and how people interact with them is quite limited. The overall effectiveness of this technology as a training tool depends upon its ability to provide appropriate learning experiences, its ability to engage the student, and its acceptability to disparate users.
This research assesses the accessibility and acceptance of a training application based on RVHT as a tool for teaching refusal avoidance skills to telephone interviewers. The application allows interviewers to practice confronting many of the common objections offered by reluctant sample members in a realistic, virtual environment. The assessment focuses on a number of key questions involving accessibility and acceptance of the training tool by interviewers, such as: Do users understand the basic features of the application? Are different users (e.g., based on ethnicity, experience level, education) equally able to use the application? When there are problems (e.g., the virtual human seems to respond inappropriately), what are user reactions? Are inappropriate responses due to a programming error, misunderstanding in the interaction, or incorrect user behavior? Are the virtual humans realistic enough for the users? And ultimately, do the users accept the virtual environment as a valid proxy for their real work environment?
Data from this analysis come from three sources: behavioral coding results from usability laboratory testing, a structured questionnaire administered to application users, and focus group data to assess qualitatively their use of and perceived effectiveness of the virtual training tool. The findings have both specific implications for the particular application tested as well as more general findings for other virtual-reality based training tools.
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