Integrating knowledge tracing and reinforcement learning in an AI-powered system for personalized ideological education
This paper presents an end-to-end AI-driven personalised ideological education system integrating psychometrically-regularised knowledge tracing, hierarchical reinforcement learning, and mixed-initiative tutoring within a scalable cloud–edge deployment model. Using public proxy datasets comprising 2847 learner profiles and 1.24 million interaction records, we evaluated the proposed system’s knowledge-tracing, curriculum-sequencing, dialogue, affect-recognition, and system-performance components, the system achieved significant gains in learning efficiency and mastery (normalised learning gain +25.5% over expert-curated curricula) and high-fidelity student modelling (KT AUC = 0.821). Real-time responsiveness was sustained at scale, with latency-critical services remaining under 100 ms at p99 and end-to-end tutoring averaging 687.4 ms under 500 concurrent users. We further introduce governance, fairness, and non-persuasive oversight mechanisms to mitigate risks of value-shaping automation in civic and ideological contexts. These results provide component-level evidence that the proposed architecture can support adaptive sequencing under benchmark conditions. Real-world validation in ideological and civic education remains necessary before claims about classroom effectiveness or learner autonomy can be made.