Trust influences perceptions of virtual humans, but not necessarily learning

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.