Adaptive Collaborative Interpretation: An AI-Enhanced Framework for Dynamic Ideological and Political Education

Abstract

This reasearch propose an Adaptive Collaborative Interpretation Framework (ACIF) that transforms ideological and political education through human-AI co-construction of dynamic pedagogical content. Traditional systems often treat AI as a passive tool, whereas our framework establishes AI as an active collaborator capable of real-time adaptation to classroom dynamics and individual learning trajectories. The core innovation lies in a BERT-based discourse modeling module that processes ideological texts and student interactions, coupled with a dynamic topic adaptation layer that identifies evolving themes through incremental clustering. Furthermore, a dual-attention neural recommender jointly considers educator inputs and AI-generated insights to personalize content delivery, while a mutual goal-setting interface optimizes educational objectives within curriculum constraints. The system integrates a modified T5 architecture for educator-AI co-editing, enabling seamless fusion of human expertise and machine analysis through confidence-weighted gating. Meta-learning techniques empower rapid adaptation to new ideological contexts, and bidirectional adapter layers ensure compatibility with conventional educational modules. Experimental validation demonstrates significant improvements in engagement and comprehension metrics compared to static approaches. This work advances the frontier of AI-augmented education by formalizing a principled framework for collaborative interpretation, offering a scalable solution to the challenges of ideological pedagogy in diverse learning environments. The proposed method not only preserves educator agency but also amplifies their capabilities through intelligent augmentation, setting a new standard for dynamic political education systems.