mlsanityData to 14 Oct 2026

Education in the Era of Neurosymbolic AI

arXiv 2411.12763 ↗First posted 16 Nov 2024cs.HC cs.AI cs.CYcited by 4

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Abstract (arXiv)

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 -- embodied characters designed to enhance learning -- as critical components of a hybrid NAI architecture. To do so, these agents can thus simulate nuanced discussions, debates, and problem-solving exercises that push learners beyond rote memorization toward deep comprehension. 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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 61 references that are in the map. First posted on arXiv 16 Nov 2024.

Builds on

17 of 61 references.

References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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  11. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 86
  12. 2024Fine-tuning and Utilization Methods of Domain-specific LLMscited by 36
5 more
  1. 2023Understanding the Effectiveness of Very Large Language Models on Dialog Evaluationcited by 12
  2. 2023An Ecosystem for Personal Knowledge Graphs: A Survey and Research Roadmapcited by 11
  3. 2024Generating Educational Materials with Different Levels of Readability using LLMscited by 9
  4. 2022Personal Knowledge Graphs: Use Cases in e-learning Platformscited by 4
  5. 2024On the Psychology of GPT-4: Moderately anxious, slightly masculine, honest, and humblecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.