XAI to Increase the Effectiveness of an Intelligent Pedagogical Agent
John Wesley Hostetter, Cristina Conati, Xi Yang, Mark Abdelshiheed, Tiffany Barnes, Min Chi
Abstract
We explore eXplainable AI (XAI) to enhance user experience and understand the value of explanations in AI-driven pedagogical decisions within an Intelligent Pedagogical Agent (IPA). Our real-time and personalized explanations cater to students' attitudes to promote learning. In our empirical study, we evaluate the effectiveness of personalized explanations by comparing three versions of the IPA: (1) personalized explanations and suggestions, (2) suggestions but no explanations, and (3) no suggestions. Our results show the IPA with personalized explanations significantly improves students' learning outcomes compared to the other versions.
Topics & Concepts
Computer scienceValue (mathematics)Personalized learningKnowledge managementEmpirical researchIntelligent agentArtificial intelligencePsychologyMathematics educationTeaching methodMachine learningCooperative learningEpistemologyOpen learningPhilosophyExplainable Artificial Intelligence (XAI)Intelligent Tutoring Systems and Adaptive LearningTopic Modeling