Purpose of the Study

This is a study of a curriculum — the syllabi, slide decks, modules, and assignment designs of an introductory GenAI course across four iterations — and not a study of the students who took the course. The purposes that follow are stated in those terms.

1.4.1 To Design, Implement, and Iteratively Refine an Introductory Course on GenAI

I piloted the introductory course on GenAI because I believe artists, designers, and engineers can all benefit from understanding how these tools work and how to use them creatively and responsibly. As someone who bridges the creative industry and engineering education, I saw a need for a hands-on, beginner friendly class where students don't become overwhelmed by technical terminology. My goal was and is to help students gain confidence using GenAI tools for class projects, educational assignments and everyday tasks, while also giving them a basic understanding of how the underlying models function (Basgen, 2023).

While designing the course, I focused on real-world examples from the creative and engineering industries. I organized content into short lessons (modules) featuring simple explanations, guided demos, and individual and collaborative assignments. After each lesson, I gathered feedback from students through written reflections to find out what they found to be successful and confusing. I then made small improvements and adjustments, tweaking instructions, adding new examples, and rearranging topics, to help the learning process run more smoothly.

Ultimately, I wanted this course to be a friendly introduction that provokes curiosity and empowers students and learners to experiment with GenAI tools in their own professional fields. By iterating on design and content based on direct feedback, I continue to strive to create a course that truly meets the needs of both creative professionals and engineering students, giving them the practical skills and confidence to explore GenAI tools on their own.

1.4.2 To Document the Evolution of the Curriculum Across Four Distinct Iterations

In the first iteration, I piloted the course with approximately 25 creative technology and design majors, students already familiar with engineering principles and excited to explore new GenAI tools. The small, specialized class allowed me to test core lessons on GenAI in a hands-on lab environment, observe where the assignment instructions read clearly and where they did not, and refine class activities and assignment designs accordingly.

For the second iteration, I opened enrollment to approximately 25 students from diverse engineering departments. By incorporating mechanical, electrical, and biomedical engineers, I learned which examples resonated across disciplines and adjusted curriculum and content to emphasize cross-departmental applications. I also scaffolded the assignments for students to use for real world applications within their domain. I provided guest lecturers from different engineering backgrounds so the students could gain knowledge from their future professional industry.

The third iteration scaled the course online to a global audience of 411 registered participants with 129 attending live, offered for free. To address varied time zones and educational backgrounds, I broke lessons into shorter modules with recorded demos. This iteration was for an hour a day for a week with different topics each day. It was branded as GenAI in Five, sponsored by the College of Engineering and Applied Science at The University of Colorado Boulder. The five GenAI topics I discussed and demonstrated were image generation, video generation, sound/music generation, research tools and human-centered AI. This curriculum assigned daily activities so participants could learn from each other's solutions and perspectives. The participants earned a badge for each assignment completed and a certificate for completion of all assignments.

In the fourth iteration, the course expanded to 4,731 registered participants worldwide, with 2,654 attending live on Day 1. At this scale, I focused on managing accessibility, inclusivity, and community support, with a team from GenAI Works who provided additional learning resources, breakout sessions, discussion forums, to reinforce learning. Each change was driven by feedback and data through polls and surveys conducted at the end of each class, ensuring that the curriculum remained practical, engaging, and relevant from pilot to global release.

1.4.3 To Surface Principles and Practices that Enable Curricula to Adapt Across Contexts and Technological Change

I believe effective curricula must be flexible, learner-centered, and data-informed to expand across different contexts and keep pace with the ever evolving GenAI technology. First, flexibility means creating modules so that future instructors can substitute in new examples, tools, or case studies without rewriting the entire course. I design each lesson with a clear learning objective and optional extra credit activities, allowing educators to customize content for art studios, engineering labs, online workshops, etc.

Secondly, a learner-centered approach ensures that diverse backgrounds and skills are respected and inclusive. I include multiple entry points for each assignment, visual tutorials, step-by-step guides and demonstrations, and open-ended challenges, so participants can choose the learning path that best matches their experience and interests. This practice not only improves engagement but also encourages peer learning, as students bring unique perspectives to group critiques and projects.

Finally, being data-informed means collecting feedback through course evaluation surveys and reflections. I review which assignment designs produced the widest range of submitted work and which assignment prompts were repeatedly flagged as unclear in course evaluation feedback. Then I refine the curriculum accordingly, whether that's clarifying instructions, reordering topics, or introducing new GenAI tools. Over time, this cycle of feedback and revision creates a living curriculum that adapts to different class sizes, cultural contexts, and GenAI advancements.