Construction of a Trinity Teaching System Based on "Computational Thinking + Artificial Intelligence + X"

Abstract

As artificial intelligence technologies continue to permeate multidisciplinary domains, how to effectively integrate computational thinking, artificial intelligence methodologies, and domain-specific knowledge within a teaching system has become a structural issue that demands urgent resolution. This study proposes a framework for constructing a trinity teaching system based on "Computational Thinking + Artificial Intelligence + X." It first analyzes the core characteristics of computational thinking and its positioning in interdisciplinary contexts, clarifies the logical starting point for embedding artificial intelligence technical elements into the teaching system, and establishes the alignment between X-domain knowledge representation and the other two components. On this basis, the study constructs the structured pathway of the system through decomposition of competency dimensions and ternary mapping relationships among knowledge units, organizes integrated teaching content via task chains, and designs system elasticity and extensible interfaces oriented toward dynamic adaptation. It further analyzes the constraints on the co-evolution of the three elements during the teaching process, establishes regulation parameters and optimization directions for cognitive load, and proposes a system self-updating capability along with a cross-cycle effectiveness evaluation framework. This system provides a computable and scalable structured solution for the organization of interdisciplinary teaching content.