| 1 | Critically evaluate the historical evolution of artificial intelligence and articulate its fundamental relationship with machine learning within the context of educational paradigms. |
| 2 | Distinguish between supervised, unsupervised, and deep learning paradigms, analyzing their theoretical foundations and applicability to educational datasets. |
| 3 | Apply core machine learning algorithms—including Linear Regression, Neural Networks, k-Nearest Neighbors (k-NN), and k-Means clustering—to model and solve complex analytical problems. |
| 4 | Develop algorithmic thinking and programming proficiency using JavaScript, specifically leveraging the p5.js and ml5.js libraries to build interactive web-based applications. |
| 5 | Design and implement sound, image, and text classification models, as well as regression analyses, utilizing the ml5.js framework to extract meaningful patterns. |
| 6 | Synthesize machine learning techniques to design innovative methodologies, interactive tools, and simulation-based learning environments for advancing science education research. |