Personalizing explanations of AI-driven hints to users' cognitive abilities: an empirical evaluation
arxiv(2024)
摘要
We investigate personalizing the explanations that an Intelligent Tutoring
System generates to justify the hints it provides to students to foster their
learning. The personalization targets students with low levels of two traits,
Need for Cognition and Conscientiousness, and aims to enhance these students'
engagement with the explanations, based on prior findings that these students
do not naturally engage with the explanations but they would benefit from them
if they do. To evaluate the effectiveness of the personalization, we conducted
a user study where we found that our proposed personalization significantly
increases our target users' interaction with the hint explanations, their
understanding of the hints and their learning. Hence, this work provides
valuable insights into effectively personalizing AI-driven explanations for
cognitively demanding tasks such as learning.
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