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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. The integration of Knowledge Graphs (KGs) with Large Language Models (LLMs), a significant and popular form of NAI, presents a promising avenue for advancing personalized instruction via neurosymbolic educational agents. By leveraging structured knowledge, these agents can provide individualized learning experiences that align with specific learner preferences and desired learning paths, while also mitigating biases inherent in traditional AI systems. 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. In this paper, we propose a system that leverages the unique affordances of KGs, LLMs, and pedagogical agents – embodied characters designed to enhance learning – as critical components of a hybrid NAI architecture. We discuss the rationale for our system design and the preliminary findings of our work. We conclude that education in the era of NAI will make learning more accessible, equitable, and aligned with real-world skills. This is an era that will explore a new depth of understanding in educational tools.
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Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy
Neurosymbolic artificial intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and the explicit, symbolic knowledge contained in knowledge graphs (KGs) to enhance explainability and safety in AI systems. This approach addresses a key criticism of current generation systems, namely, their inability to generate human-understandable explanations for their outcomes and ensure safe behaviors, especially in scenarios with unknown unknowns (e.g., cybersecurity, privacy). The integration of neural networks, which excel at exploring complex data spaces, and symbolic KGs representing domain knowledge, allows AI systems to reason, learn, and generalize in a manner understandable to experts. This article describes how applications in cybersecurity and privacy, two of the most demanding domains in terms of the need for AI to be explainable while being highly accurate in complex environments, can benefit from neurosymbolic AI.
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- PAR ID:
- 10508530
- Publisher / Repository:
- IEEE Internet Computing
- Date Published:
- Journal Name:
- IEEE Internet Computing
- Volume:
- 27
- Issue:
- 5
- ISSN:
- 1089-7801
- Page Range / eLocation ID:
- 43 to 48
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/MIC.2023.3299435
