Exploring the Impact of AI on Science Teaching: Effective Pedagogical Strategies for Developing Digital Literacy Skills

Digital innovations are proliferating throughout society and becoming a larger part of human life. It is difficult to conceive of an aspect of human behavior that is not being affected by digital technology. The recognized impacts are the apparent lack of digital literacy and lack of awareness of ethical conduct regarding artificial intelligence (AI) in teachers, which can be limitations for teaching and learning in a digital environment. This project aims to raise teachers' awareness of ethical conduct as it relates to AI and the broader digital landscape to help teachers amplify their digital literacy skills to improve the success of learning and support students' appropriate usage of technology. The first step in this study is to assess and investigate the use of AI practices to enhance high school teachers' digital literacy skills.The paper then discuss the important role that AI digital images play in enhancing high school teacher performance. Also followed by an embedded smart device as the arrangement in structuring a framework based on smart categorization and methods based on the Framework of Educational Learning Outcomes (FOLR). The purpose of this Framework is to assess and compare the digital literacy and teaching performance of high school teachers to classification methods like support vector machines (SVM) and decision tree. The data shows that the SVM method achieved 78% accuracy on identifying teacher digital literacy skills after 600 round of the method while the FOLR method achieved 81%. Meanwhile, the geographic complexity of the SVM-oriented and FOLR-based smart literacy enhancement algorithms are calculated to be 45 and 22, respectively. Particularly, with more rounds, the FOLR method achieves improved accuracy and lower geographical complication in measuring teachers' pedagogical digital literacy skills. As a result, the application of AI techniques is extremely successful in improving digital imaging innovation and improving the performance of image identification in academic training.