Adapting the Artificial Intelligence Assessment Scale for K-12 Education: A Developmental Framework for Age-Appropriate AI Integration

Abstract The emergence of generative artificial intelligence (GenAI) has disrupted educational assessment across all levels. While the AI Assessment Scale (AIAS) has succeeded in higher education contexts, K-12 environments present unique developmental, pedagogical, and contextual factors requiring significant adaptation. This theoretical framework paper examines AIAS evolution and proposes a K-12-specific adaptation accounting for cognitive development stages, regulatory constraints, and equity considerations. Through systematic literature analysis revealing only two published studies addressing K-12 AIAS implementation, we identified critical gaps necessitating this adaptation. Mapping AIAS levels against Piagetian cognitive stages, Vygotskian scaffolding principles, cognitive load theory, Kolberg’s Moral Development Theory, and executive function development research, this paper proposes five adapted levels with progressive implementation structures. The proposed theoretical framework maintains AIAS core principles of transparency and pedagogical intentionality and provides developmentally appropriate pathways ensuring AI integration supports rather than supplants foundational skill development.