Lived Experiences and Challenges of Public Secondary Teachers in Utilizing Artificial Intelligence (AI) in Teaching Science: Basis for Training Design

Abstract

Artificial Intelligence (AI) is increasingly integrated into education, yet its effective use in public secondary science classrooms depends on teachers’ experiences, competencies, available resources, and institutional support. This study explored the lived experiences and challenges of public secondary science teachers in utilizing AI in teaching science and used the findings as the basis for developing a context-responsive training design. Anchored on the Input–Process–Output model, the study employed a qualitative phenomenological design involving eight science teachers from Mobo National High School selected through complete enumeration. Data were collected using a validated researcher-made semi-structured interview guide and analyzed through thematic analysis. Findings revealed three major dimensions of teachers’ lived experiences: integration of AI in science teaching practice, improved classroom interaction and learning environment, and professional growth and pedagogical development. Teachers used AI tools for lesson planning, instructional material development, assessment preparation, organization of content, simplification of scientific concepts, and learner engagement. However, AI integration was constrained by unstable internet connectivity, inadequate devices and technological resources, insufficient practical training, limited institutional and technical support, unclear guidelines, and concerns regarding content reliability, privacy, plagiarism, academic dishonesty, student overreliance, and critical thinking. The study concludes that AI can enhance the efficiency and quality of science instruction when used as instructional support rather than as a replacement for teachers. It recommends sustained professional development, stronger institutional and technological support, responsible-use guidelines, content-verification practices, and implementation of the proposed school-based AI integration training design. The study primarily supports SDG 4 – Quality Education and SDG 9 – Industry, Innovation and Infrastructure by strengthening teacher capacity and responsible educational technology integration. Its sustainability impact centers on educational, technological, digital, and institutional sustainability by promoting competent, ethical, equitable, and context-responsive AI use in science education.