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

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.

Advancing credit mobility through stakeholder-informed AI design and adoption

Transferring from a 2-year to a 4-year college is crucial for socioeconomic mobility, yet students often face challenges ensuring their credits are fully recognized, leading to delays in their academic progress and unexpected costs. Determining whether courses at different institutions are equivalent (i.e., articulation) is essential for successful credit transfer, as it minimizes unused credits and increases the likelihood of bachelor's degree completion. However, establishing articulation agreements remains time- and resource-intensive, as all candidate articulations are reviewed manually. Although recent efforts have explored the use of artificial intelligence to support this work, its use in articulation practice remains limited. Given these challenges and the need for scalable support, this study applies artificial intelligence to suggest articulations between institutions in collaboration with the State University of New York system, one of the largest systems of higher education in the US. To develop our methodology, we first surveyed articulation staff and faculty to assess adoption rates of baseline algorithmic recommendations and gather feedback on perceptions and concerns about these recommendations. Building on these insights, we developed a supervised alignment method that addresses superficial matching and institutional biases in catalog descriptions, achieving a 5.5-fold improvement in accuracy over previous methods. Based on articulation predictions of this method and a 61% average surveyed adoption rate among faculty and staff, these findings project a 12-fold increase in valid credit mobility opportunities that would otherwise remain unrealized. This study suggests that stakeholder-informed design of AI in higher education administration can expand student credit mobility and help reshape current institutional decision-making in course articulation.