Reframing Teacher Professionalism in AI-Supported Science Education: Digital Integration and Automation Design as Core Competencies

The integration of artificial intelligence (AI) into education presents both opportunities and challenges, particularly in inquiry-based and context-sensitive science classrooms. Although commercial AI tools are increasingly available, it remains unclear how teachers can move from passive consumers of technology to active designers of AI-mediated instruction. This position paper proposes two professional competencies needed for teachers to maintain and enact expertise in AI-enhanced environments, digital integration competency and automation design competency. Digital integration competency refers to teachers’ ability to collect, connect, and analyze educational data for instructional purposes. Automation design competency refers to teachers’ ability to design digital automated workflows that are aligned with pedagogical goals. Both competencies depend on data preparation and require teachers to exercise professional judgment in evaluating and transforming instructional data. This conceptualization extends the TPACK framework by addressing the practical demands of AI integration in science teaching. These competencies support teachers’ professional agency, responsible innovation, and capacity to resist the risks of technological colonization in the age of AI. Drawing on classroom examples, international policy frameworks, and current research, this paper offers a conceptual foundation for rethinking teacher expertise in AI-integrated science education. It ultimately reimagines the teacher not as a passive user of technology, but as a designer of AI-supported instructional environments.