Literature Review

This section reviews existing research on three interrelated topics: the use of GenAI in education, the curriculum-as-research approach, and frameworks for teaching ever-changing technologies. Basing research on studies from engineering education, instructional design, and technology-integrated learning, this literature review highlights the essential findings and identifies holes and gaps that this dissertation addresses.

1.2.1 GenAI in Education

GenAI, applications that produce text, images, code, or other artifacts based on data-driven models, has rapidly entered educational settings. Early studies focused on using GenAI as a tutor or content generator. For example, Huang et al. (2023) demonstrated how AI chatbots can answer student questions in large online courses, improving response times and perceived instructor presence. More recent work explores GenAI's role in creative assignments. Dehouche and Dehouche (2023) illustrated how art and design courses used text-to-image models to help students brainstorm design concepts, reporting increases in creative ideation and student confidence. However, most implementations highlight tool demonstrations rather than the underlying pedagogy, leaving a space in understanding how to integrate GenAI into curriculum design systematically.

Researchers have also examined ethical and equity concerns. Baker and Hawn (2022) investigated how GenAI assignments might reproduce biases present in training data and recommended inclusive design practices, such as diverse prompt examples and reflective discussions, to mitigate harm. Kelly and Sullivan (2023) found that students with limited technical backgrounds can struggle with blurred AI behaviors, emphasizing the need for transparency and scaffolded explanations.

Despite these advances, there remains a lack of cohort studies documenting full course development cycles. Few works trace iterative refinements across multiple offerings or analyze how student feedback drives curriculum development. This dissertation fills that void by presenting a systematic account of course design, implementation, and revision in GenAI education.

1.2.2 Frameworks for Teaching Disruptive Technologies

Disruptive technologies, innovations that fundamentally change industries and the skills they demand, create a unique challenge for educators. Christensen, Grossman, and Hwang's (2009) concept of disruptive innovation has been adapted to showcase the need for curricula that can change rapidly as new tools appear.

Modular course design supports flexible content updates by breaking curricula into independent units or modules. Salmon's (2002) e-tivities model demonstrates how self-contained online activities can be updated individually, such as substituting in new case studies on developing AI ethics, without revising the entire syllabus.

Community engagement involves co-creating curriculum with learners to ensure relevance and ownership. Healey and Jenkins (2009) show that partnerships between students and faculty, involving learners in curriculum planning and review, lead to deeper engagement and improved learning outcomes. By involving students in selecting GenAI tools and refining module objectives, educators build a shared responsibility for curriculum development.

Although each element, modularity, transferability, and engagement, has strong support, few frameworks integrate all three into a cohesive methodology for the fast-moving technical field. This dissertation proposes an integrated framework that combines curriculum-as-research documentation, iterative teaching inquiry, and these three pillars to address the specific demands of GenAI education.