###### Abstract  
Computer vision education is increasingly important in modern technology curricula; yet, it often lacks a systematic approach integrating both theoretical concepts and practical applications. This study proposes a staged framework for computer vision education designed to progressively build learners' competencies across four levels. This study proposes a four-staged framework for computer vision education, progressively introducing concepts from basic image recognition to advanced video analysis. Validity assessments were conducted twice with 25 experts in the field of AI education and curricula. The results indicated high validity of the staged framework. Additionally, a pilot program, applying computer vision to acid-base titration activities, was implemented with 40 upper secondary school students to evaluate the effectiveness of the staged framework. The pilot program showed significant improvements in students' understanding and interest in both computer vision and scientific inquiry. This research contributes to the AI educational field by offering a structured, adaptable approach to computer vision education, integrating AI, data science, and computational thinking. It provides educators with a structured guide for implementing progressive, hands-on learning experiences in computer vision, while also highlighting areas for future research and improvement in educational methodologies.  
computer vision education; staged framework; computational thinking; data science; symbolic AI; neural network-based AI +
Footnote †: journal: Medical  
particularly effective for tasks involving logical reasoning, knowledge representation, and the manipulation of abstract ideas, which makes it suitable for areas such as expert systems and language comprehension [5]. However, symbolic AI faces challenges in learning from large amounts of raw data and adapting to new, unseen situations due to its reliance on predefined rules and structures [6].  
Computer vision technology is critical to both present and future information technology industries, and its study is fundamental for students. In an environment where neural network-based AI, such as deep learning, is central to computer vision technology, students need to develop practical skills to solve problems using real data, in addition to theoretical knowledge. However, most current educational curricula are either theory-centric or overly focused on practice, lacking a systematic educational approach that integrates neural network-based AI and symbolic AI. Despite technological advancements, there is a lack of systematic educational frameworks capable of effectively teaching computer vision technologies [3]. Especially in primary and secondary education, teaching computer vision requires a staged learning approach that aligns with students' levels and educational goals.  
The integration of data science and computational thinking is essential in computer vision education. Data science provides the foundation for collecting, preprocessing, analyzing, and modeling image data, enabling students to learn and evaluate computer vision models [7]. Computational thinking plays a vital role in developing students' abilities to systematically solve problems. Elements of computational thinking such as decomposition, abstraction, pattern recognition, and algorithm design help students logically approach computer vision problems [8].  
This study aims to develop a staged framework for computer vision education by integrating neural network-based AI and symbolic AI in each stage. This framework incorporates AI, data science, and computational thinking to enhance problem-solving abilities. By emphasizing a staged framework, we design each stage to allow learners to simultaneously experience theory and practice through this integration.