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

Yerin Kwak, Zachary A. Pardos

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.

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

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.
Paper Structure (18 sections, 2 figures)

This paper contains 18 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Instructional Design and Learning Sciences
  3. Core Focus
  4. Methodological Approaches
  5. Applications in Educational Contexts
  6. Limitations of ID and LS and the Need for Integration
  7. From Previous Efforts to Emerging Opportunities in Generative AI
  8. The RIGID Framework
  9. Analysis
  10. Design
  11. Implementation
  12. Evaluation
  13. Challenges and Considerations
  14. Hallucinations
  15. The Role of Human Expertise
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Framework for research-integrated instructional design. The RIGID framework integrates Learning Sciences perspectives into instructional design processes across four iterative phases, with AI serving as a mediating mechanism. The inner ring represents core instructional design activities, the middle ring illustrates how AI supports their integration, and the outer ring depicts contributions from the Learning Sciences. Together, the framework shows an iterative cycle in which insights from each phase inform and refine subsequent instructional design decisions.
  • Figure 2: Operationalization of the analysis and design phases in the RIGID framework. Micro-level insights from instructional design, together with meso- and macro-level insights from the learning sciences, are distilled into prompt components for the AI, which then optimizes prompts, prototypes materials, and simulates learners to create contextually grounded and pedagogically aligned instructional resources.