As Generative AI (GenAI) models continue to gain prominence, a new frontier is emerging in the field of computer science education. Results from initial anonymous surveys reveal that nearly half (48.5%) of our students now turn to GenAI for academic assignments, highlighting its growing role in modern education. With educators facing challenges in creating dynamic and unique course content, the potential of GenAI becomes evident. It offers not only a quicker method for content development but also paves the way for diversified, high-quality educational resources, countering traditional cheating methods and catering to varied student needs. Key questions thus arise: "How can GenAI assist instructors in creating meaningful content and problems quickly, and can it reduce the instructional staff's workload?" Addressing the first question, we unveil the "GenAI Content Generation Framework". This novel tool equips educators to tap into the prowess of GenAI for course content design. The framework presents a systematic and practical blueprint for generating university-level course material through chat-based GenAI. Drawing from our first-hand experiences, we provide strategic guidance on formulating inquiries and organizing GenAI sessions to elicit quality content that aligns with specific educational goals and context. Our work stands apart by outlining a specific workflow and offering concrete suggestions for harnessing GenAI in course material development, backed by a strong case for its adoption. Armed with the framework and insights presented in this paper, educators and course content developers can move forward with assurance, tapping into GenAI's vast potential for innovative content creation.

Citation: Ethan Dickey and Andres Bejarano, A Model for Integrating Generative AI into Course Content Development, https://arxiv.org/abs/2308.12276.