Neuro-Symbolic AI in Education: A Systematic Literature Review of Architectures, Pedagogical Integration, and Trustworthy Intelligent Learning Systems

Recent advances in educational artificial intelligence have been driven largely by large language models and other neural architectures. However, these systems continue to face challenges in factual reliability, pedagogical consistency, explainability, and governance. Neuro-symbolic artificial intelligence overcomes these limitations by combining the neural learning capabilities with symbolic reasoning to create more reliable intelligent learning systems. Yet there has been no systematic literature review that has systematically synthesized the architectural, pedagogical and governance aspects of this emerging field. We systematically searched the IEEE Xplore, Scopus, and Web of Science databases up to 10 July 2026, according to the PRISMA 2020 guidelines. A validated hybrid screening process, incorporating automated screening and required manual screening, yielded 40 studies from 3,492 records. To better reflect architecture trends, we also built an inventory of neuro-symbolic educational systems on the framework-level, which contains 92 systems. The review reveals that 60.0% of the studies included in the analysis also demonstrated architectural integration, pedagogical grounding and governance or explainability. At the framework level, 71.1% of classifiable systems adopted either unified representation or sequential integration strategies, indicating strong convergence toward a limited set of architectural paradigms. Less than half of the identified frameworks (46.7%) however, reported similar quantitative performance benefits, which restricts the ability to make robust cross-study comparisons. In general, the intersection of education and neuro-symbolic AI is heading toward a handful of well pedagogically grounded, but relatively under-developed, architectures in the area of governance and explainability. These findings provide a foundation for architectural standardization, transparent evaluation practices, and the development of trustworthy neuro-symbolic intelligent learning systems.