Research Approach and Framework
This study is curriculum-design research. The unit of analysis is the curriculum itself — the syllabi, modules, slide decks, assignment designs, and revisions I authored as the instructor across four iterations of the Introduction to GenAI course — not the students who took the course. Student-facing inputs (course feedback surveys, written reflections, learner-produced artifacts) functioned as course evaluation under my normal instructor authority and informed when and why I revised the curriculum, rather than serving as research data about students. The methodological framework that follows is built around that orientation: design-based research and curriculum-as-research applied to the instructor-authored artifacts, with course evaluation inputs treated as the contextual trigger for iterative revision. The human-subjects scope of this work is named explicitly in §2.6.5.
2.1.1 Iterative Teaching as Inquiry: Each Course Offering as Instruction and Research
In this course, each new cohort functioned as an ongoing experimentation. Every lesson, activity, and assignment was both a learning experience for students and a qualitative data point. By structuring teaching around questions and exploration, I witnessed what was successful, what needed improvement, and how different contexts influenced learning.
At the start of each class, I identified clear design questions: Which GenAI examples drew the most discussion in class? Where did the assignment instructions repeatedly need to be clarified? How did different delivery formats (in-person workshops, hybrid sessions, or fully online modules) affect the artifact-design choices I had to make? With these questions in mind, I gathered evidence through several different methods. Course evaluation surveys recorded written responses about clarity, relevance, and course pacing. Classroom notes recorded which demonstrations took the most time to walk through. Assignment-submission patterns indicated when an assignment prompt was working as designed and when the prompt needed to be rewritten. In-person and virtual discussion threads surfaced new GenAI tool discoveries and recurring questions that more formal end-of-module surveys did not catch.
After each module, I reviewed these different data points to identify commonalities. If a prompt-engineering exercise continually led to confusion, I rewrote the instructions or added a step-by-step demonstration video. When peer critiques consistently led to strong cross-disciplinary ideas, I created more structured reflection around those activities. If an assignment showed mastery of one concept but weakness in another, I designed additional mini-lessons or offered optional office hours to support those areas.
This planning, teaching, observing, and reflecting cycle operated at two levels. Within a single cohort, it guided small, day-to-day or week-to-week adjustments that improved clarity and workflow. Between cohorts, it informed larger curriculum revisions, such as removing and adding modules on new GenAI tools or restructuring the order of topics based on repeated patterns in the data.
By documenting all lesson and assignment changes linked to the collected data, I maintained a clear record of curriculum updates. This record traced the course's development over time and served as a basis for sharing best practices with other educators. Through this process, iterative teaching as practice made the course an ongoing research project, with continuous improvement guided by coursework, reflection, and collaboration with learners.
2.1.2 Curriculum as Scholarly Contribution: The Evolving Syllabus, Assignments, and Pedagogy as Primary Research Products
I managed the course syllabus, assignments, and teaching methods as strong research artifacts rather than consistent instructional materials. Whatever change I created in the lessons, whether it was modifying an assignment prompt, reorganizing a module sequence, or adding a collaborative peer critique session, was carefully documented. Over time, these documented iterations form an audit trail showing how different pedagogical strategies and tool choices were retained, revised, or replaced in subsequent iterations of the curriculum.
I analyzed these artifacts by looking for repeated revision patterns. For example, when I added instructional videos on specific GenAI tools to a module, I observed in subsequent submissions that the same recurring errors appeared less often and that the assignment prompt did not need to be repeated as frequently — both signals that the revised slide deck and instruction set were clearer. Adding structured peer reviews became a stable feature of the next-iteration assignment design, which I retained based on course evaluation feedback that flagged peer review as a useful element of the iteration.
To share the results, I presented at various academic conferences, provided GenAI workshops for professional development throughout the community, guest lectured in classrooms, special events, and clubs. These venues allowed me to discuss how modular curriculum design supported quick implementation of new GenAI tools, and to display case studies where hands-on projects appeared to lead to successful student innovations.
Collaboration with colleagues helped strengthen this scholarly contribution. I sought feedback on draft syllabi, observed co-taught sessions, and co-authored reflective papers examining which adaptations worked across different institutions. I also used GenAI tools as a co-collaborator to help edit and brainstorm. The co-creative process provided best practices for inclusive design, such as providing multiple options within lessons and ensuring accessibility for learners with varied technical backgrounds.
The adaptive curriculum became the main research product. By approaching teaching as a reflective practice and sharing both successes and challenges, I helped develop principles that can guide future education for Introduction to GenAI courses. These contributions aimed to help educators worldwide design adaptive, learner-centered programs that kept pace with the ever-changing GenAI industry, supporting students in developing both practical skills and flexible mindsets needed for future creative and technical challenges.