The RIGID Framework: Research-Integrated, Generative AI-Mediated Instructional Design
Yerin Kwak, Zachary A. Pardos

TL;DR
The RIGID framework offers a systematic, AI-mediated approach to integrating learning sciences research into instructional design workflows, enhancing evidence-based, context-sensitive educational practices.
Contribution
This paper introduces RIGID, a novel framework that systematically incorporates learning sciences research into instructional design using generative AI at all stages.
Findings
Framework enables research integration across ID phases
Leverages generative AI for operational support
Maintains human expertise in design process
Abstract
Instructional Design (ID) often faces challenges in incorporating research-based knowledge and pedagogical best practices. Although educational researchers and government agencies emphasize grounding ID in evidence, integrating research findings into everyday design workflows is often complex, as it requires considering multiple context-specific demands and constraints. To address this persistent gap, this paper explores how research in the learning sciences (LS) can be systematically integrated across ID workflows and how recent advances in generative AI can help operationalize this integration. While ID and LS share a commitment to improving learning experiences through design-oriented approaches in authentic contexts, structured integration between the two fields remains limited, leaving their complementary insights underutilized. We present RIGID (Research-Integrated, Generative…
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Taxonomy
TopicsInnovative Teaching and Learning Methods · Teaching and Learning Programming · Online Learning and Analytics
