UTILIZATION OF AI AMONG SCIENCE HIGH SCHOOL TEACHERS: A COMPARATIVE ANALYSIS

Abstract

This research study explores the use of artificial intelligence (AI) by high school science instructors and attempts to determine how various instructor-related factors are used to differentiate levels of AI usage in terms of instructional practices. The study uses a quantitative-comparative methodology, in which a survey was completed by science instructors from publicly funded secondary schools in Dumanjug, Cebu in order to gather information regarding science instructors' experiences related to AI integration. The responses were analyzed through a comparative analysis to identify the significant variables that differentiate between instructors by their level of AI usage. The findings indicate that technological expertise, years of teaching experience, instructors' perception of the usefulness of AI and instructors' attitudes toward innovation are all significant factors of differentiation between instructors who have high levels of AI usage and those who have low levels of AI usage. More specifically, science instructors who have high technological proficiency levels and positive perceptions of AI integration into their practice are more likely to use AI tools in their lesson plans, classroom instruction, and assessment effectively. The study also identifies several barriers to the complete use of AI in science instruction, including a lack of resources and a lack of training. In a more general sense, the results from this study indicate that in order to provide increased professional development and support services at the school-level to provide science instructors with confidence to increase the level of use of AI in their instructional practices, these will ultimately improve instructional outcomes and increase student engagement in science instruction. The study also provides three areas of contribution to the literature; (a) demonstrates that teacher characteristics influence AI usage; (b) investigates AI usage in the specific context of science instruction among secondary education; and (c) creates an empirically based model that can assist educators, policymakers and administrators with the integration of AI into classroom instructions. The understanding of these relationships will help schools utilize AI based educational tools to improve teacher practice and student outcomes in science education.