Learning Design with AI: Adapting the LISA Framework for K-12

Presentation of the Paper in the Proceedings described as: 

Efforts to prepare educators for AI-supported learning design aren growing rapidly, yet current terminology often lacks the precision to distinguish between qualitatively different forms of AI integration, obscuring critical differences in decision-making authority and interaction dynamics. To address this ambiguity, this paper adapts the LISA Framework (Level of autonomy and Integration Scope of AI in learning design) from its original higher education context for consideration in K–12 school settings and teacher training. By transferring the framework's two-dimensional typology—distinguishing between levels of AI autonomy and scopes of interaction—we aim to establish a structured vocabulary that may enable pre-service and in-service teachers to systematically analyse and critically reflect on AI configurations within their specific pedagogical contexts. For this paper, we adapt the framework from higher education to the situation and examples from the school sector and present mostly fictive use cases for illustration. The paper presents potential measures for a future validation of the LISA framework.

Presented by Gerald Geier.