Education in the Era of Neurosymbolic AI
Chris Davis Jaldi, Eleni Ilkou, Noah Schroeder, Cogan Shimizu

TL;DR
The paper discusses how neurosymbolic AI can revolutionize education by enabling highly personalized, adaptive learning experiences through pedagogical agents that simulate complex interactions and support deep understanding.
Contribution
It proposes a hybrid NAI architecture utilizing pedagogical agents to enhance personalized education and discusses preliminary findings supporting this approach.
Findings
Pedagogical agents can simulate nuanced educational interactions.
NAI enables fine-grained diagnosis of student understanding.
Preliminary results show potential for increased accessibility and personalization.
Abstract
Education is poised for a transformative shift with the advent of neurosymbolic artificial intelligence (NAI), which will redefine how we support deeply adaptive and personalized learning experiences. NAI-powered education systems will be capable of interpreting complex human concepts and contexts while employing advanced problem-solving strategies, all grounded in established pedagogical frameworks. This will enable a level of personalization in learning systems that to date has been largely unattainable at scale, providing finely tailored curricula that adapt to an individual's learning pace and accessibility needs, including the diagnosis of student understanding of subjects at a fine-grained level, identifying gaps in foundational knowledge, and adjusting instruction accordingly. In this paper, we propose a system that leverages the unique affordances of pedagogical agents --…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI)
