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.