We propose self-organizing and simplifying neuro-fuzzy networks (NFNs) to yield transparent human-readable policies by exploiting fuzzy information granulation and graph theory. Deriving from social network analysis, we retain only the frequent-yet-discernible (FYD) patterns in NFNs and apply them to reward-based scenarios. The effectiveness of NFNs from FYD patterns is shown in classic control and a real-world classroom using an intelligent tutoring system to teach students.

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.