Educational Progress Based On Science Technology Engineering And Psychology For Scholars Using Ai And Machine Learning

This retraining plan entails training courses like AI contents and ethics issues related to biased AI for teachers to be well informed on AI matters and ethics, as well as training them on how they can adopt AI strategies in their STEM areas. Five-day cycles comprise Data Analytics, Decision Trees, Machine Learning, RNN Artificial Networks, and Transfer Learning. In addition to that, these modules include realized activities, online tool interactions, and the real AI model code in Google Colab notebooks to develop, train, and evaluate AI models. These are developed through learning, which has taken a series of steps to get to the level of creating them. Teachers of science, psychology, physics, engineering, and math subjects are eligible for participation in Teacher Professional Development. In this study, the author recommends modifications to the format of instructor professional development sessions. Finally, we present the advantages and the difficulties of preparing academics to use AI in disciplinary speeches. In this research study, we shall establish a high level of association between formative testing-cognitive and psychomotor testing scores as well as mid-test scores and grades than final test scores. Data from the psychomotor assessment input model improved accuracy to a higher level, with Random Forest having a higher accuracy; it has been exemplified that early learning advancement can be enhanced as examples of using Artificial Intelligence & Machine Learning in STEM for educational advancement.

کلیدواژه ها:

Machine Learning ، Artificial Intelligence ، STEM ، Educational Progress ، AI Model