Contributions to theory and research

This study contributes to several strands of educational theory and research. First, it extends constructivist and self-determination theory perspectives by showing how a curriculum can be designed to support motivation, autonomy, and meaningful learning in both credit-bearing university contexts and large, non-credit global offerings focused on GenAI. In all four iterations, the curriculum was designed to invite learners to connect GenAI projects to their own interests, disciplines, and professional goals, and to share their work with peers. The artifact trail across iterations shows that the curriculum was designed and refined to offer learners genuine choices in tools and topics, scaffolded tasks for competence-building, and spaces to connect with others, and that these design choices were retained as the curriculum scaled from for-credit to large global formats. The design choices are consistent with what self-determination theory predicts will support high-quality motivation; whether the predicted motivational outcomes occurred at the learner level is a question for follow-up research with an IRB-reviewed protocol.

Second, the study deepens work on curriculum-as-research and design-based research by treating the Introduction to GenAI course as a research object over multiple iterations. The analysis drew on slide-deck and module revisions and other instructor-authored curriculum artifacts as the primary data sources, with course evaluation feedback used as a contextual trigger explaining why specific revisions occurred (§2.6). Iterative cycles of planning, teaching, observing, and reflecting were used not only to improve the course locally, but also to surface broader curriculum-design principles such as modularity, learner choice, and the value of universal GenAI concepts. This approach shows how a course in a rapidly changing technical area can serve both as an educational experience and as a living, evolving research artifact that generates knowledge about curriculum design.

Third, the study adds to literature on disruptive technologies in education by offering an empirically documented model from one institutional context for teaching a black swan technology in engineering. Rather than treating GenAI as a temporary add‑on or a single lecture topic, the course was designed from the start to handle volatility through modular design, continuous feedback, and shared responsibility between instructor and learners. The findings illustrate how these design choices can make room for unpredictability while still providing structure, ethical grounding, and opportunities for deep learning. This model can inform future work on how engineering education and related fields respond to sudden, large‑scale technological shifts.