Amid the rapid advancement of artificial intelligence (AI) and the digital transformation of schooling, computational thinking has become a foundational competency for K-12 learners and an organizing principle for AI education. Existing research suggests that students need not only programming knowledge, but also the ability to analyze problems, reason with data and models, and evaluate intelligent systems responsibly. However, current K-12 AI education often remains fragmented, with insufficient curriculum progression, tool-oriented instruction, uneven teacher preparation, and limited attention to learning processes, transfer, and ethical AI use. As a conceptual and integrative framework article, this paper synthesizes policy guidance and recent instructional research on integrating computational thinking into K-12 AI education. Building on prior Chinese scholarly discussion of Computational Thinking 2.0, we adopt and extend this perspective to connect rule-based algorithmic reasoning with data- and model-centered AI problem solving. We propose a teacher–student–AI collaborative framework and a multi-level pathway covering tiered curriculum design, blended human–AI teaching, scaffolded project-based learning, AI agent-supported feedback, teacher development, and process-oriented, transfer-sensitive, and ethics-aware assessment. We argue that AI education should move beyond tool demonstration and code production toward authentic problem solving, model understanding, responsible human–AI collaboration, and reflective innovation. The framework offers guidance for cultivating students’ computational thinking, AI literacy, and creative problem-solving capacity, while identifying directions for empirical validation.
Zhong et al. (Fri,) studied this question.