Enabling the widespread utilization of the Artificial-Social-Agent (ASA) Questionnaire, a research instrument to comprehensively assess diverse ASA qualities while ensuring comparability, necessitates translations beyond the original English source language questionnaire. We thus present Dutch and German translations of the long and short versions of the ASA Questionnaire and describe the translation challenges we encountered. Summative assessments with 240 English-Dutch and 240 English-German bilingual participants show, on average, excellent correlations (Dutch ICC M = 0.82, SD = 0.07, range [0.58, 0.93]; German ICC M = 0.81, SD = 0.09, range [0.58, 0.94]) with the original long version on the construct and dimension level. Results for the short version show, on average, good correlations (Dutch ICC M = 0.65, SD = 0.12, range [0.39, 0.82]; German ICC M = 0.67, SD = 0.14, range [0.30, 0.91]). We hope these validated translations allow the Dutch and German-speaking populations to evaluate ASAs in their own language.@en

In this paper, we report on the multi-year Intelligent Virtual Agents (IVA) community effort, involving more than 80 researchers worldwide, researching the IVA community interests and practises in evaluating human interaction with an artificial social agent (ASA). The effort is driven by previous IVA workshops and plenary IVA discussions related to the methodological crisis on the evaluation of ASAs. A previous literature review showed a continuous practise of creating new questionnaires instead of reusing validated questionnaires. We address this issue by examining questionnaire measurement constructs used in empirical studies between 2013 to 2018 published in the IVA conference. We identified 189 constructs used in 89 questionnaires that are reported across 81 studies. Although these constructs have different names, they often measure the same thing. In this paper, we, therefore, present a unifying set of 19 constructs that captures more than 80% of the 189 constructs initially identified. We established this set in two steps. First, 49 researchers classified the constructs in broad theoretically based categories. Next, 23 researchers grouped the constructs in each category on their similarity. The resulting 19 groups form a unifying set of constructs, which will be the basis for the future questionnaire instrument of human-ASA interaction.

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