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

2026-03-13

AI Summary

This work addresses the persistent gap between learning sciences research and instructional practice by proposing the RIGID framework, which systematically embeds learning science theories into all phases of instructional design—analysis, design, implementation, and evaluation. For the first time, generative AI is integrated throughout this process to intelligently retrieve, adapt, and apply evidence-based findings while preserving the central role of human experts in critical decision-making. The framework offers a clear, scalable structure that significantly enhances the capacity of instructional design to incorporate scientific evidence and adapt to diverse contextual demands. By synergizing theoretical grounding, empirical evidence, and artificial intelligence, RIGID establishes a novel paradigm for instructional design that is evidence-based, actionable, and intelligent.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Machine Learning: Structured Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Instructional Design
• Learning Sciences
• Research Integration
• Generative AI
• Evidence-Based Practice

Innovation

• Generative AI
• Instructional Design
• Learning Sciences
• Research Integration
• RIGID Framework