Background and Rationale

1.1.1 The Rise of Generative AI as a Black Swan Event

I remember the first time I saw a Generative Artificial Intelligence (GenAI) application create images that looked almost as if they were made by human artists. The moment made me feel curious, excited but also nervous. GenAI seemed to appear almost overnight, changing creative work and education in ways none of us expected. As a visual arts educator who helps students explore and create artistic ideas, I observed tools such as OpenAI's GPT-3 and DALL·E produce original stories and visuals with incredible efficiency and accuracy. GenAI was more than a new technology, it was what Taleb (2007) calls a "black swan" event, an unexpected development with major and lasting effects.

My advisor, Professor Tom Yeh, asked me to redesign parts of my curriculum to help students understand how these tools work and how to use them ethically and responsibly. With these GenAI tools starting to appear, new questions about authorship, originality, and what it means to be creative were brought up daily. In the creative industry and classroom, I now present GenAI content as a starting point for brainstorming and human imagination rather than a replacement for it. These tools have changed how I teach and encourage new conversations about ethics, technical skills, and the future of creative work in the industry and classroom (Brown et al., 2020).

1.1.2 Unprecedented Speed and Breadth of Adoption

I've never witnessed a technology spread so quickly. Within weeks of GPT-3's release, social media, classrooms, and the creative industry were chaotic with people testing prompts and sharing AI-generated drafts (Brown et al., 2020). Educators who hesitated to introduce AI into their curriculum, now assign students to use the tools as collaborative partners. Artists who spent months/years on concept sketches began using DALL·E to explore dozens of ideas in minutes (Ramesh et al., 2021). Even small non-profits, Title 1 and local schools have free or low-cost access to these powerful GenAI models, which make this technology accessible to more people than ever imagined. This quick, global adoption, along with the variety of users, from teachers, politicians, and industry leaders, proves how GenAI truly shook up every professional industry and educational institution almost overnight.

1.1.3 Rapid Technological Evolution, Demanding Responsiveness

I've observed GenAI move and evolve at record pace, with each new model release bringing capabilities we hadn't even imagined weeks prior. When GPT-3 amazed us with their technical abilities, it felt cutting-edge. Then GPT-4 arrived, expanding into image creation, coding assistance, and reasoning in ways that made my course materials obsolete overnight (OpenAI, 2023). As a creative educator, I've learned to build flexibility into my curriculum, planning time each week to explore and research the latest tools and adjust my teaching materials on the fly. This constant change in GenAI tools, forces me to stay curious and responsive, demonstrating for students how to learn alongside technology rather than fall behind it.

1.1.4 Why Engineering Needs to Address GenAI Now

I've spent years teaching students how to combine art and technology, and I've never worked with a tool so powerful as GenAI. In engineering classrooms, this new technology can automate routine coding tasks, generate design prototypes, and even suggest original solutions. With these new technological advancements, the tool also introduces new risks around accuracy, bias, and copyright infringement, which are issues that need to be addressed.

By integrating GenAI into our curriculum now, we give future engineers the chance to learn how to successfully use these tools responsibly and evaluate and critique their outputs. When students experiment with code generation prompting, they discover both the efficiencies and the downfalls, such as subtle errors that can cause safety or ethical concerns when GenAI models reproduce biased data (Brown et al., 2020).

Teaching GenAI tools and skills helps engineers collaborate more effectively with other disciplines which can create interdisciplinary innovations (Ramesh et al., 2021). It also prepares students for a job market where fluency in AI-driven workflows is becoming an essential.

Addressing GenAI today means building an adaptable curriculum that combines hands-on projects and demonstrations, ethics discussions, and regular updates on the latest GenAI models. This approach not only equips students with cutting-edge competencies but also instills a mindset of continuous learning, which is crucial in a world where technological evolution never stops (OpenAI, 2023). Engineering education that embraces GenAI now will help empower graduates to shape technology's future.

1.1.5 The Opportunity: A Pioneering Introductory Course Designed, Taught, and Studied as a Scholarly Contribution

I was honored to pilot an Introduction to Generative AI course through the school of Engineering and Applied Science at The University of Colorado Boulder in the spring of 2024. I continued to teach the course for a second time during the spring of 2025. During the courses, every student project was used for collective data discovery. From week one, students experimented with a range of tools, such as text generators like ChatGPT and Microsoft Copilot, image generators like DALL·E and Midjourney, music generators like Soundful and Suno, video generators like Runway and Pika, recording prompts, outputs, and reflections in a specific module for their assignments in Canvas, which is a learning management system (Bommasani et al., 2021).

Each module centers on a different topic: prompt engineering, image, video, sound, human centered AI, and vibe coding. Students compare how different models handle the same assignment, identify patterns in specific styles and errors, and discuss ethical considerations around bias and ownership. Their assignments become the foundation for a future research paper that outlines best practices across tools and highlights areas for improvement (Marcus & Davis, 2020).

By positioning the course as a scholarly contribution, students learn GenAI skills and concepts through hands-on demonstrations rather than just lectures. Weekly classes introduce emerging research, such as new approaches to prompt engineering, updates on existing GenAI models, and new GenAI tools, while providing guest lecturers and workshops from industry and academia deliver real-world insights. Assessment focuses on students' assignments and research contributions, data analyses, and reflective essays, that help draft sections of the scholarly paper, so by the end of the semester, everyone has built both practical skills and a publishable artifact.

The pioneering structure of the course not only provides aspiring creatives and engineers with GenAI tool fluency but also trains them as significant scholars, ready to shape GenAI's technological future through evidence-based exploration.