The RIGID Framework: Research-Integrated, Generative AI-Mediated Instructional Design

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 AI-Mediated Instructional Design), a unified framework that integrates LS research across ID workflows spanning analysis, design, implementation, and evaluation phases, while leveraging generative AI to mediate this integration at each stage. The RIGID framework provides a systematic approach for enabling research-integrated instructional design that is both operational and context-sensitive, while preserving the central role of human expertise.