Authors - Danial Hooshyar, Yeongwook Yang, Raija Hamalainen, Tommi Karkkainen Abstract - Recent advances in general intelligence paradigms, particularly large language models (LLMs), have accelerated the adoption of artificial intelligence (AI) in education. However, recent studies show that LLMs often exhibit shallow adaptivity and struggle to reliably model learners’ evolving knowledge over time. Similar to other deep neural networks, their opaque nature and susceptibility to bias and spurious correlations can limit alignment with pedagogical principles, raising concerns regarding transparency, fairness, and trustworthiness, particularly in educational settings classified as high-risk under the EU AI Act. While responsible AI use has received increasing attention, responsible AI development—an essential prerequisite for responsible use—remains comparatively overlooked. This paper presents Estonia’s five-year experience applying hybrid neural-symbolic AI (NSAI) methods in educational contexts to operationalize responsible AI development. By integrating symbolic knowledge directly into neural learning processes, the presented approaches aim to support human-centered, interpretable, and pedagogically grounded AI systems. In contrast to recent trends emphasizing loosely coupled symbolic components around closed large-scale models, the Estonian approach focuses on tighter integration between neural and symbolic representations to improve transparency, learner modeling, and educational alignment. Through five illustrative case studies developed across educational applications ranging from student performance prediction to deep knowledge tracing, the paper demonstrates how hybrid neural-symbolic methods can support explainability, pedagogical robustness, and responsible AI objectives in education. Finally, the paper discusses key lessons learned, remaining challenges, and future opportunities for applying neural-symbolic AI in education.
