INTEGRATING AI TO CREATE AN ENGAGING AI LITERACY COURSE

ABSTRACT

This methodology paper documents the design and development of an AI literacy course for higher education that addresses critical gaps in preparing faculty and students for responsible AI engagement. Grounded in backward design principles (Wiggins & McTighe, 2005) and AI literacy frameworks (Ng et al., 2021), the six-module asynchronous course integrates curated multimedia resources with task-specific Custom GPTs as pedagogical agents to scaffold learning across conceptual, practical, ethical, and societal competencies. This work contributes to scholarship of teaching and learning (SoTL) by operationalizing "transparency by design" requiring learners to document AI use, verify outputs, and critique limitations thereby embedding ethical accountability within coursework. The paper addresses methodological challenges including the black box problem of large language models, platform drift, and tensions between AI assistance and authentic learning. By centering learner agency and struggle as essential to knowledge construction, the methodology deliberately requires human engagement rather than passive consumption. This replicable, open educational resources (OER)-enabled blueprint offers institutions a scalable model for systematic AI literacy preparation across academic, professional, and civic contexts.