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Trusting and Learning From Virtual Humans that Correct Common Misconceptions
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AI
DOI：10.1177/07356331221139859.png)
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摘要
En 中文
Virtual humans are on-screen characters that are often embedded in learning technologies to deliver educational content. Little research has investigated how virtual humans can be used to correct common misconceptions. In this study, we explored how different types of narrative structures, refutation text and expository text, influence perceptions of trust, credibility, and learning outcomes. In addition, we conducted exploratory analyses examining how different measures of trust and credibility are related to each other and how these measures may mediate learning outcomes. Results showed that the type of narrative used did not influence any measure. However, the trust and credibility measures, while related to one another, were measurably distinct. In addition, only perceptions of message trust were significantly related to learning. Perceptions of message trust did not mediate learning outcomes, but were significantly predictive of learning at nearly the same effect as prior knowledge.
Keyword:
trust
pedagogical agent
virtual human
explainability
refutation
credibility
learning
期刊
IF:
4.9
论文数:
1.6K
被引数:
4.0K
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