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.

翻译：教学设计（ID）在融入基于研究的专业知识与教学最佳实践方面常面临挑战。尽管教育研究者与政府机构强调教学设计应基于证据，但将研究发现整合到日常设计工作流程中通常十分复杂，因为这需要考虑多种情境特定的需求与约束。为弥合这一长期存在的鸿沟，本文探讨了如何在学习科学（LS）研究中系统性地整合教学设计工作流程，以及生成式人工智能的最新进展如何助力实现这种整合。虽然教学设计学与学习科学都致力于通过真实情境中的设计导向方法来改善学习体验，但两领域间的结构化整合仍显不足，导致其互补性见解未能得到充分利用。我们提出RIGID（研究整合式生成人工智能辅助教学设计）这一统一框架，该框架将学习科学研究整合到涵盖分析、设计、实施与评估阶段的教学设计全流程中，并利用生成式人工智能在每一阶段辅助实现这种整合。RIGID框架为开展兼具可操作性与情境敏感性的研究整合式教学设计提供了系统化路径，同时保留了人类专业知识的核心地位。