Validating student AI competency self-efficacy (SAICS) scale and its framework

Abstract Nurturing student artificial intelligence (AI) competency is crucial in the future of K-12 education. Students with strong AI competency should be able to ethically, safely, healthily, and productively integrate AI into their learning. Research on student AI competency is still in its infancy, primarily focusing on theoretical and professional discussions, along with qualitative investigations. This two-stage study aims to propose an AI competency framework for students and confirm the reliability and validity of its scale—student AI competency self-efficacy (SAICS)—in K-12 education. In stage 1, we used a three-round Delphi study to propose the framework and its scale. The framework has eight dimensions: interdisciplinary learning with AI, assessment with AI, decision-making with AI, data, ethics and AI, designing AI, multimedia creation with AI, human-centric learning, and confidence with AI. Each dimension contains four items. In stage 2, we involved 448 students to validate the scale using confirmatory factor analysis and model comparisons. The analyses showed that the scale is consistent across male and female students. The SAICS scale comprises 32 items and addresses eight dimensions of AI competency. Researchers can use the framework and SAICS to design their interventions and correlational research associated with student AI competency. Teachers can use them to develop learning outcomes for AI-based learning activities, and policymakers can use them to establish national AI standards.