论文标题

孩子和父母在对话代理商中想要和感知什么?朝着透明,值得信赖,民主化的代理商

What Do Children and Parents Want and Perceive in Conversational Agents? Towards Transparent, Trustworthy, Democratized Agents

论文作者

Van Brummelen, Jessica, Kelleher, Maura, Tian, Mingyan Claire, Nguyen, Nghi Hoang

论文摘要

从历史上看,研究人员致力于分析成人对技术的怪异观点。这意味着我们可能没有针对儿童和来自非键国家的技术制定适当的技术。在本文中,我们从各个国家对新兴技术的角度分析了孩子和父母:对话代理。我们的目标是更好地了解参与者对代理商,合作伙伴模型的信任及其对“理想未来代理人”的想法,以便研究人员可以更好地为这些用户设计。此外,我们使孩子和父母能够通过教育研讨会对自己的代理进行编程,并随着参与者创造和学习代理人的看法而发生了变化。该研究的结果(n = 49)包括孩子们感觉人的感觉比父母更像是人性化,温暖和可靠的,参与者比父母或朋友对正确的信息的信任人如何,如何将其理想的代理人描述为比父母更像是人造的,而与父母相比,与孩子更倾向于通过与父母更加专注于有趣的特征,友好/友好的功能和代理人设计,而不是其他成果。我们还讨论了结果的潜在代理设计含义,包括设计师如何通过专注于设计代理的能力和可预测性指标来增强对代理的适当信任,并在代理信息源方面提高透明度。

Historically, researchers have focused on analyzing WEIRD, adult perspectives on technology. This means we may not have technology developed appropriately for children and those from non-WEIRD countries. In this paper, we analyze children and parents from various countries' perspectives on an emerging technology: conversational agents. We aim to better understand participants' trust of agents, partner models, and their ideas of "ideal future agents" such that researchers can better design for these users. Additionally, we empower children and parents to program their own agents through educational workshops, and present changes in perceptions as participants create and learn about agents. Results from the study (n=49) included how children felt agents were significantly more human-like, warm, and dependable than parents did, how participants trusted agents more than parents or friends for correct information, how children described their ideal agents as being more artificial than human-like than parents did, and how children tended to focus more on fun features, approachable/friendly features and addressing concerns through agent design than parents did, among other results. We also discuss potential agent design implications of the results, including how designers may be able to best foster appropriate levels of trust towards agents by focusing on designing agents' competence and predictability indicators, as well as increasing transparency in terms of agents' information sources.

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