Theoretical Framework

Building on the research questions, this section outlines the narrow theoretical framework that guides how I designed, iteratively refined, and analyzed the Introduction to GenAI course across four iterations. This study uses a small set of ideas to guide it. First, I draw on constructivist learning and motivation theories, especially self-determination theory, to explain differences between students enrolled in for-credit university classes and adults enrolled in free, online global courses. Second, I use ideas from curriculum-as-research and design-based research to treat the course itself as something I can study and improve over time. Third, I pay close attention to ethics, accessibility, and my own role and biases as a teacher and researcher.

1.7.1 Learning and Motivation Across Contexts

The study is constructed in a practical, constructivist view of learning in which students build knowledge through active engagement with GenAI tools, peers, and original creative and engineering tasks rather than passively receiving information (Schunk, 2020). The Introduction GenAI course curriculum integrates social constructivist theory, where knowledge is built through active problem-solving and engagement with tools and communities. This theoretical alignment supports the course's emphasis on experiential learning, collaborative peer evaluation, and the application of real-world projects across various global instructional environments (Prince & Felder, 2006).

Across the four iterations, participants engaged in different formal and informal structures, including university courses for credit and large-scale global offerings that emphasized badges and certificates rather than transcripts. Table 1 summarizes these structures below:

Table 1. Four iterations of course structures.

The framework relies on self-determination theory (SDT), which assumes that high-quality motivation depended on supporting learners' needs for autonomy, competence, and relatedness (Deci & Ryan, 1985; Ryan & Deci, 2000). In the first two iterations, creative technology and design majors and then mixed-discipline engineering undergraduates (mechanical, electrical, biomedical, physics, and others) engaged in formal, accredited courses. Their learning experiences were shaped by a combination of extrinsic motivators (grades, transcripts, degree progress) and intrinsic motivators (curiosity, creative exploration, desire to build portfolio pieces). The curriculum adapted by incorporating intrinsically meaningful projects, open-ended GenAI media explorations, cross-disciplinary design challenges, and student-led "teach outs", within an assessment structure that still met institutional expectations. This design explicitly focused on supporting autonomy, competence, and relatedness aligned with SDT, even within a graded environment (Ryan & Deci, 2000).

When the course pivoted online to reach 411 and then 4,731 registered adults worldwide, the motivational account changed. Participants did not receive university credit and participated with varied professional backgrounds, time zones, and no prior technical preparation, often driven by interest in GenAI, professional development, or a general love of learning. To support these intrinsic motivations, the online versions emphasized short, focused modules; daily, achievable tasks; visible recognition through badges and certificates; and spaces where participants could share work and learn from a diverse international community. These design choices were intended to create a sense of autonomy (choice in projects and tools), competence (scaffolded tasks), and relatedness (community interaction), consistent with SDT's emphasis on basic psychological needs (Ryan & Deci, 2000). This course structure focused on making engagement feel worthwhile even without formal credit, while still encouraging deep exploration of various GenAI tools.

Across all course iterations, I intentionally tried to design for intrinsic engagement even as the balance between intrinsic and extrinsic motivators shifted. In the university Introduction to GenAI courses, assignments were created as contributions to a shared research project and as artifacts students could reuse in portfolios or job applications, not only as graded tasks. In the online offerings, where external pressure was lower, I focused on creating activities connected to each person's real life and offered options so adults could choose projects that aligned with their work, creative interests, or personal goals. These motivational considerations, informed by SDT and constructivist views of meaningful learning, are key to how I interpret learner participation and outcomes across formal and informal settings (Schunk, 2020; Ryan & Deci, 2000).

1.7.2 Curriculum-as-Research and Iterative Inquiry

A second pillar of the framework is curriculum-as-research, supported by educational design research and iterative teaching-as-inquiry practices (McKenney & Reeves, 2018; Kemmis et al., 2014). Instead of viewing the syllabus, assignments, and teaching methods as constant, I managed each one as something I could carefully record, explore, and improve. This approach is similar to work in STEM education where teachers and researchers repeatedly design and revise curriculum with students to improve learning and to build new knowledge about teaching (Burrows et al., 2018; Wendell et al., 2021).

Each of the four iterations; pilot, refinement, initial online scaling, and global launch, functioned as a planning, teaching, observing, and reflection cycle. In the smaller, in-person classes, I recorded how creative technology and design majors and then mixed engineering cohorts responded to modules on prompt engineering, image and video generation, sound, human-centered AI, and vibe coding, and used their feedback and assignments to refine instructions, scaffolds, and examples. As the course expanded online, these cycles extended to synchronous demos, asynchronous discussion forums, which allowed for continuous improvements in pacing, explanation, tools, and activity design.

Design-based research and curriculum-as-research also informed how I conceptualized scaling from a 25-student pilot to approximately 4,700 registered participants worldwide. Modular design, treating each unit as an independent "building block" with clear learning objectives and flexible tools, enabled quick replacements of examples and platforms as GenAI technologies continued evolving (Salmon, 2002; Redecker & Punie, 2017). Student and participant input, peer critiques, and "teach out" sessions created opportunities for learners to shape the evolving curriculum, resonating with work on student-faculty collaborations and co-created curricula (Healey & Jenkins, 2009). This perspective helps explain how the course maintained its core goals while adapting to different institutional settings, delivery modes, and learner populations.

1.7.3 Technology, Ethics, and Access as Lenses

Because GenAI is what Taleb (2007) calls a "black swan" technology; sudden, very disruptive, and unpredictable; the framework also uses ideas from research on innovation, educational technology, ethics, and equity. Studies about disruptive technologies address that courses need to stay flexible, adaptable and easy to update, rather than relying on syllabi that change slowly over many years (Christensen et al., 2009; Selwyn, 2021). This view directly shaped my choice to design modules that can easily incorporate new tools, models, and ethics examples without needing to rebuild the entire course each iteration.

Frameworks for ethics and equity in AI education show that GenAI is powerful and risky, raising concerns about bias, transparency, and unequal access to technology (Baker & Hawn, 2022; UNESCO, 2021). These ideas formed four main themes in this course and study: industry, education, accessibility, and ethics. Industry-focused activities explored how GenAI changes professional workflows including questions ownership, and intellectual property (Bommasani et al., 2021; Marcus & Davis, 2020). Education-focused activities addressed GenAI as a learning tool, prompting discussions about academic integrity and the future of teaching (Huang et al., 2023; Kelly & Sullivan, 2023). Accessibility-focused activities concentrated on differences in technology access, leading to assignments that explored user experiences across different groups of people and locations (Tomlinson, 2014; UNESCO, 2021). Ethics discussions and assignments emphasized fairness, safety, and responsible use, encouraging students to reflect on potential harms and best practices of GenAI.

These four themes also helped explain how different groups of learners, from creative technology and design, and engineering students to global adult learners, experienced the course, and how the curriculum evolved as GenAI tools and conversations continued to change.

1.7.4 Reflexivity, Positionality, and Bias

Finally, the framework includes reflexivity and positionality, recognizing that my role as both instructor and researcher, along with my background in creative technology, design, and engineering education, shapes what I notice, prioritize, and document in the course (Schön, 1983; Finlay, 2002). Qualitative research practices guided me to keep revision logs, analytic notes, and reflections alongside more traditional data sources such as surveys, student work, and activity data (Yin, 2014).

Because this study is qualitative and self-reflective, and based on curriculum materials I created, I recognized that my excitement for GenAI and focus on modular, student-centered design could influence how I interpreted the data. To address this concern, I used multiple data sources, gathered feedback from colleagues in engineering and related fields, and documented events of uncertainty, surprise, and failure in my notes (McKenney & Reeves, 2018; Finlay, 2002). These steps did not eliminate bias but made it more visible and open to rigorous examination. In summary, this framework brings together ideas about how people learn and what motivates them, especially self-determination theory, along with curriculum-as-research and design-based approaches that support ongoing, adaptive changes to the course. It also uses technology, ethics, and accessibility as different perspectives to understand GenAI as a rapidly changing technology and includes a reflexive approach to qualitative research. These different perspectives supported how the GenAI course was designed, implemented, and scaled across diverse contexts, and how learners' motivations and backgrounds influenced its changes over each iteration.