## 5 Discussion
This approach allowed learners to directly implement object detection algorithms suitable to their programming proficiency. Using advanced block coding, learners could grasp complex concepts without being hindered by syntax, while Python coding offered a deeper dive into programming and algorithmic implementation. This process deepened learners' programming skills while encouraging active participation in data preprocessing and model training. Experts evaluated that learners could deeply understand the core concepts of computer vision by actually implementing object detection models in this stage. This was effective in cultivating practical problem-solving abilities rather than mere theoretical learning. However, some learners faced difficulties due to the challenges of text-based programming languages [36]. Additional support measures, such as scaffolding and differentiated instruction, are necessary to address this issue. Future research could propose personalized learning support that provides assistance based on individual learning levels [42]. In the fourth stage--video analysis and action recognition--teachers had the option to choose between block coding combined with physical computing (e.g., Scratch 3 or mBlock V5 with Arduino or other hardware) and utilizing deep learning frameworks like TensorFlow 2 or PyTorch 2, based on the learners' proficiency. This flexibility enabled learners to study advanced computer vision technologies through practical exercises suited to their skill levels, either by integrating coding with hardware for a tangible learning experience or by engaging with deep learning frameworks for those ready for more complex challenges. Experts assessed with high validity that learners could acquire high-level technical skills by building and analyzing actual deep learning models in this stage. However, the complexity of deep learning frameworks posed high difficulty levels for some learners [43]. This indicates the need for staged support tailored to learners' levels in future educational courses, requiring improvements in instructional design. Incorporating scaffolding techniques and providing comprehensive resources can help mitigate these challenges [44]. The pilot implementation, while focusing primarily on the second and third stages of the framework, provided valuable insights into its effectiveness. Students demonstrated significant improvements in their understanding of how computer vision and AI are applied in inquiry-based activities. This suggests that our staged approach successfully integrates computer vision concepts with practical scientific inquiry, enhancing both technical skills and subject knowledge. The integration of neural network-based AI and symbolic AI in this staged framework is exemplified in the acid-base titration activity. In this context, neural network-based AI is represented by the linear regression model that predicts pH values based on RGB data from images of BTB indicator solutions. This model learns patterns from data without the explicit programming of rules, characteristic of neural network approaches. Concurrently, symbolic AI is incorporated through the process of analyzing RGB values from BTB indicator images using OpenCV 4 and critically examining the reasons behind the machine learning model's pH predictions. For instance, students use OpenCV 4 to extract RGB values from the indicator images, applying predefined algorithms and rules to process the visual data. This step represents a rule-based, symbolic approach to image analysis. Subsequently, students engage in a logical analysis of the machine learning model's predictions, utilizing their understanding of acid-base chemistry and color theory to interpret and validate the results. This analytical process involves creating explicit rules and heuristics to explain the relationship between RGB values and pH levels. The integration occurs as students use the symbolically derived RGB data to train the neural network model, and then apply symbolic reasoning to interpret the model's outputs.