Personalizing explanations of AI-driven hints to users' cognitive abilities: an empirical evaluation
This work addresses the challenge of enhancing engagement and learning outcomes for specific student groups in Intelligent Tutoring Systems, representing an incremental improvement in personalization techniques.
The study tackled the problem of low engagement with AI-driven hint explanations among students with low Need for Cognition and Conscientiousness by personalizing explanations, resulting in significant increases in interaction, understanding, and learning for these target users.
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.