AI-assisted course design through accelerators and multipliers in online higher education

This article presents a practitioner-led inquiry into the integration of large language models (LLMs) into the course design process in higher education. Recognizing the complex, often invisible labor of instructional design—especially in online environments—the study introduces a set of AI-assisted tools structured as an adaptable pipeline of “Accelerators” and “Multipliers.” These tools are not intended to automate instructional design but to catalyze creativity, support reflective practice, and amplify inclusive, student-centered learning. Developed and tested in real institutional contexts, each tool is a design artifact grounded in iterative prompt engineering and pedagogical intent. The article explores their practical applications, pedagogical implications, and the cultural shifts required for ethical and effective AI integration in teaching and learning. Findings suggest that LLMs, when paired with human judgment, can serve as powerful design partners—provoking new insights, streamlining workflow, and expanding the range of instructional possibilities without diminishing academic rigor or faculty agency.