generative ai in digital education: a case study of content co-creation in a design thinking mooc

Integrating generative artificial intelligence (genai) into educational materials development presents opportunities and challenges in education, particularly in massive open online courses (moocs). this study explores the role of genai in developing content for moocs using a design thinking mooc as a case study. it assesses genai-generated instructional materials for content accuracy, depth, and engagement potential while analyzing the level of human intervention required for pedagogical quality. using perplexity pro as the genai tool, the study finds that genai efficiently generates structured drafts, fictional learning scenarios, and key takeaways. however, significant limitations emerge in genai's ability to differentiate complex domain specific concepts, develop high quality assessment items, and ensure pedagogical alignment. human intervention remains fundamental for enhancing conceptual depth, refining instructional clarity, and fostering learner engagement. based on these insights, the study proposes a framework for genai-assisted content creation in mooc design, outlining a structured approach to integrating genai while maintaining educational rigor. the framework highlights four interdependent phases: content planning & genai preparation; genai-generated content creation; expert review & refinement; and testing & iterative improvement. the study further presents guidelines and best practices for mooc designers, providing practical recommendations for leveraging genai effectively without compromising instructional quality. this research contributes to the growing literature on ai-driven education, providing practical guidelines for mooc designers seeking to optimize genai-driven content development.