## 3 Methods
### Pilot Implementation Results  
The results of the pre- and post-program surveys were analyzed using a 5-point Likert scale. Statistical analysis revealed significant improvements across all questions, as outlined in Table 12.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Question** & **\(M\)** & **\(SD\)** & **CVR** \\ \hline Clarity of Educational Objectives & Are the educational objectives at each stage clearly set and easily understandable for learners? & 4.52 & 0.50 & 1.00 \\ \hline Learner Engagement & Do the learning activities at each stage encourage active participation from learners? & 4.64 & 0.48 & 1.00 \\ \hline Appropriateness of Practical Activities & Are the practical activities provided at each stage effective in achieving the learning objectives? & 4.64 & 0.48 & 1.00 \\ \hline Provision of Feedback & Is it possible to provide appropriate feedback to learners during the learning process at each stage? & 4.60 & 0.49 & 1.00 \\ \hline Validity of Evaluation Methods & Can the evaluation methods at each stage accurately measure learners’ achievement? & 4.68 & 0.47 & 1.00 \\ \hline Practicality of Technology Application & Are the technical elements and tools presented at each stage applicable in actual educational environments? & 4.52 & 0.50 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of the second expert validity assessment.  
For Question 1, which assessed students' perception of the interest level of computer vision education, the mean score increased from 3.500 (\(SD\) = 0.877) before the program to 4.575 (\(SD\) = 0.636) after the program. The \(t\)-value of 6.345 and the \(p\)-value of less than 0.001 indicate a statistically significant improvement in students' interest in the subject. In Question 2, which examined the perceived usefulness of computer vision education, the mean score improved from 3.600 (\(SD\) = 0.900) before the program to 4.625 (\(SD\) = 0.628) after the program. A \(t\)-value of 6.176 and a \(p\)-value of less than 0.001 suggest that students found the program significantly more beneficial following participation. Question 3 focused on students' knowledge of the color changes of acid-base indicators. The pre-program mean score of 2.300 (\(SD\) = 1.091) increased significantly to 4.175 (\(SD\) = 0.813) after the program, with a t-value of 9.531 and a \(p\)-value of less than 0.001, demonstrating a notable gain in scientific understanding. Question 4, which assessed students' ability to predict pH values using a computer vision machine learning model, showed a marked improvement. The mean score rose from 2.300 (\(SD\) = 0.939) before the program to 4.075 (\(SD\) = 0.917) after the program, with a t-value of 8.834 and a \(p\)-value of less than 0.001, reflecting a significant enhancement in students' practical application of AI. Question 5, which evaluated students' understanding of how computer vision and AI are applied in inquiry-based activities, demonstrated an increase from a pre-program mean score of 1.900 (\(SD\) = 1.109) to a post-program mean score of 3.275 (\(SD\) = 1.213). The \(t\)-value of 5.561 and the \(p\)-value of less than 0.001 indicate a statistically significant improvement in students' comprehension of the integration of AI and computer vision in scientific exploration.