Developing an AI Educator Readiness Matrix for AI-Enabled Science Laboratory: Bridging Generations in Science Education

The increasing integration of artificial intelligence (AI) in science education has created new opportunities for enhancing laboratory instruction while simultaneously requiring educators to possess the competencies necessary for effective AI adoption. This study assessed educators’ readiness to implement AI in science laboratory instruction and developed an AI Educator Readiness Matrix (AERM) to support institutional decision-making and professional development. Employing a descriptive quantitative research design, data were collected from 23 science educators via a validated survey. Descriptive statistics, including frequencies, percentages, weighted means, and rankings, were used to analyze the respondents’ profiles and levels of readiness across five dimensions: knowledge of AI concepts, technical skills, instructional confidence, institutional support, and attitude toward AI use in science education. The findings revealed that the respondents were predominantly Millennial educators with six to ten years of teaching experience who had primarily acquired AI knowledge through webinars and online seminars. Overall, educators were found to be ready to implement AI in science laboratory instruction (M = 3.59). Among the readiness dimensions, attitude toward AI (M = 3.89), knowledge of AI concepts (M = 3.83), and institutional support (M = 3.47) were rated as ready, whereas technical skills (M = 3.36) and instructional confidence (M = 3.39) were only moderately ready, indicating the need for targeted capacity-building initiatives. Based on these findings, the study developed the AI Educator Readiness Matrix (AERM), a practical assessment and decision-support tool that enables educational institutions to evaluate educator readiness, identify competency gaps, and design evidence-based professional development interventions prior to implementing AI-enabled science laboratory instruction. The matrix provides a systematic framework for strengthening AI integration while promoting sustainable, ethical, and pedagogically sound science laboratory practices.