The Readiness of Integrating AI (Artificial Intelligence) in Teaching Science: Perspectives of Private Higher Educational Institution Science Teachers in Bayombong, Nueva Vizcaya

Abstract

Successful integration of Artificial Intelligence (AI) in science education depends largely on teachers’ readiness to effectively utilize emerging technologies in their instructional practices. Artificial Intelligence (AI) is the term used for systems that can learn from data, do tasks, and act like humans when it comes to reasoning, problem-solving, and making decisions. In this context, teachers' readiness includes teachers' perceived competence, resource accessibility, professional development and training, attitudes toward AI integration, and perceived barriers. This study sought to assess the preparedness of science educators at a private higher education institution in Bayombong, Nueva Vizcaya, for the integration of artificial intelligence in science instruction. Specifically, it examined their demographic profile, level of readiness across key dimensions, perceived barriers and challenges, differences when grouped by profile variables, and the relationship between readiness and perceived barriers. A quantitative descriptive-correlational research design was utilized, employing an adapted and validated questionnaire based on the Technology Acceptance Model (TAM). The study was conducted at a private higher education institution in Bayombong, Nueva Vizcaya, with the participation of 32 science educators. The data were analyzed using mean, standard deviation, t-test, ANOVA, Pearson correlation, and regression analysis. The results showed that teachers were somewhat ready, had mostly positive attitudes toward AI, and thought they were competent enough. Resource availability and professional development were somewhat accessible; however, significant obstacles, including time limitations, data privacy issues, and ambiguous institutional policies, were recognized. There were no significant differences when the groups were grouped by sex, but age and years of teaching experience had a significant effect on readiness and perceived barriers. There was also a strong negative correlation between readiness and perceived barriers. The study concludes that teachers are willing to use AI, but to make it work better in science education, they need more training, resources, and policy changes.