Knowledge graphs as pedagogical bridges for symbolic reasoning in hybrid AI systems: a perspective

Artificial intelligence (AI) has achieved extraordinary progress in recent years, yet this progress reveals a deep educational and epistemic imbalance. Neural architectures have mastered prediction but often obscure the grounds of their outputs. This Perspective argues that knowledge graphs (KGs) are more than a technical advance: they are an intellectual bridge between symbolic and neural paradigms, and a pedagogical opportunity to reform university-level AI curricula. The true frontier of explainable AI is educational, not only technological. By reintroducing symbolic reasoning into advanced AI curricula and professional training, we can prepare students who design, build, deploy, and evaluate AI systems to understand and justify system outputs. The focus is higher education for future developers, deployers, and auditors of AI systems, not general AI literacy for everyday users of AI tools. Through historical analysis, theoretical synthesis, and pedagogical reflection, we show that knowledge graphs are not only computational infrastructures but also catalysts for cognitive transformation in how we teach, learn, and conceptualize intelligence.