Abstract Researchers have investigated the extent to which features of virtual humans (e.g., voice) influence learning. However, only recently have researchers began to question how trust influences perceptions of virtual humans and learning with a virtual human. In this study, we use unsupervised machine learning (k-means clustering) to examine the extent to which learners trust a virtual human with different voices. General trust attitudes toward a virtual human were measured using a validated questionnaire after participants watched videos of the virtual human presenting educational information. The results indicated that a virtual human with a low-quality text-to-speech (TTS) voice had the most variation in perceived trust, followed by a high-quality TTS voice and a recorded human voice. In addition, trust significantly influenced perceptions of the virtual human persona in the TTS conditions, whereas it had little influence on measures of persona in the recorded human voice condition. Finally, trust had little influence on learning outcomes in any of the conditions. While previous findings have shown that trust can vary between voice conditions, the results presented here show that different types of voices led to different trust responses even within voice conditions, raising important implications for instructional designers.

中文翻译：

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信任影响虚拟人的感知，但不一定影响学习

摘要 研究人员调查了虚拟人的特征（例如，语音）影响学习的程度。然而，直到最近，研究人员才开始质疑信任如何影响对虚拟人的看法以及与虚拟人一起学习。在这项研究中，我们使用无监督机器学习（k-means 聚类）来检查学习者信任具有不同声音的虚拟人的程度。在参与者观看虚拟人展示教育信息的视频后，使用经过验证的问卷来衡量对虚拟人的一般信任态度。结果表明，具有低质量文本到语音 (TTS) 语音的虚拟人在感知信任方面的差异最大，其次是高质量的 TTS 语音和录制的人类语音。此外，信任显着影响了 TTS 条件下虚拟人物角色的感知，而它对记录的人类语音条件下的人物角色测量几乎没有影响。最后，在任何情况下，信任对学习结果几乎没有影响。虽然之前的研究结果表明信任可能因语音条件而异，但此处呈现的结果表明，即使在语音条件下，不同类型的语音也会导致不同的信任反应，这对教学设计人员具有重要意义。