Significance of the Study

The contributions stated below are curriculum-design contributions: they concern how a course of this kind can be built, adapted, and scaled, rather than claims about the students who took any specific offering.

1.5.1 Novelty: Among the First Systematic Documentations of an Introductory GenAI Course in Engineering Education

This study is significant because it provides one of the first systematic, step-by-step records of how to design, implement, and modify an introductory course on GenAI within engineering education. By documenting every iteration, from a pilot with approximately 25 creative technology and design majors to successive lessons with diverse engineering cohorts, an online cohort of 411 registered participants (129 attending live), and finally a global launch with 4,731 registered participants (2,654 attending live on Day 1), this research provides a successful outline for course creation. It details how to select real-world examples, structure hands-on activities, and integrate reflective exercises and assignments so that learners with varying backgrounds can comprehend and apply GenAI concepts.

GenAI progresses at a rapid pace, and instructors often struggle to keep content current and accessible. This study addresses that challenge by introducing modular curriculum design: organizing the syllabus into independent units or modules that can be individually updated when new models, platforms, or ethical considerations arrive. It also allows for a collaborative learning experience, where students contribute to new GenAI tool discoveries, recommendations, and feedback that form subsequent iterations. Simple visual graphics, demonstration videos and workshops on how to use new GenAI tools, further prove how small changes can drastically improve clarity and engagement. Educators can adapt these methods to their own contexts, whether in art studios, engineering labs, online classrooms, or corporate professional development training settings.

Moving beyond the course design, this research uses the curriculum as a scholarly contribution. Every syllabus revision, assignment modification, and curriculum adjustment is documented and analyzed to reveal underlying principles that support effective learning. The resulting framework combines creative practice with technical instruction, demonstrating how hands-on projects, collaborative critiques, and iterative feedback create deeper understanding and creative innovation. By sharing both successes and challenges through conference presentations, peer-reviewed articles, and faculty and community workshops, this work broadens the conversation in engineering education and offers evidence-based strategies for teaching rapidly changing technologies.

This study provides educators and institutions with a tested model for developing flexible, inclusive, and continuously improving GenAI courses. It fills an essential need for beginner-level resources, makes sure that courses remain relevant amongst technological advances, and contributes new knowledge on how adaptive curricula can prepare students for the real-world creative and technical challenges produced by GenAI.

1.5.2 Scholarly Contribution: Shows how to Design Curricula that are Inherently Adaptive to Context and Technological Change

This study makes an original contribution to engineering education by providing a detailed, evidence-based framework for designing curricula that is adaptive to instructional context and the accelerated development of GenAI technologies. The framework emphasizes three interrelated practices.

Modular curriculum design splits the course into independent units with clearly defined learning objectives, assignments and activities, and assessment criteria. Each module is organized indicating the GenAI tools, models, and ethical topics it covers. When a new GenAI model or platform emerges, instructors can update just the affected module, refining lecture slides, substituting demonstrations, and refreshing assignments, without disrupting the overall course structure. This targeted approach to updates minimizes downtime, reduces work load on instructors, and makes sure that students always engage with the latest GenAI technology and research.

Student-led feedback places students as active contributors to the curriculum process. Throughout each cohort, learners share GenAI tool discoveries, use cases, and successes and challenges through discussion forums, surveys, and peer-review sessions. Instructors verify these contributions through hands-on activities and analysis of student work. This co-creative process accelerates content refinements but also encourages an agile mindset among participants, preparing them to become lifelong learners in a constantly changing field.

The curriculum is built on a platform independent conceptual framework. Rather than teaching students a single GenAI tool, the curriculum focuses on universal principles and skills, such as responsible use, ethics, fairness, transparency, safety, and effective prompt engineering that apply across GenAI platforms. This universal approach guarantees that learners can quickly adapt their skills when new GenAI tools gain recognition or existing ones progress.

By documenting every syllabus revision, pedagogical refinement, and update process as research data, this work provides actionable insights into what pedagogical strategies best support creativity, technical understanding, and inclusive learning.

This study contributes to the knowledge of engineering education by offering a reproducible, scalable model for adaptive curriculum design. Educators at research universities, institutions, corporate training programs, and online learning platforms can adopt these principles to build or refine GenAI courses that remain relevant, inclusive, and effective among the technical advancements.

1.5.3 Practical Impact: Provides a Model for Scaling from Small Pilot Classes to Large Global Audiences

This study delivers a model for scaling an introductory GenAI course from a small pilot to a large global audience. The process begins with a specialized pilot of approximately 25 creative technology and design majors, allowing instructors to test core lessons against course evaluation feedback and refine hands-on activities. Insights from this phase inform the design of foundational modules, such as prompt engineering exercises and ethical case studies, that are clear, engaging, and relevant to learners with diverse backgrounds and technical skills.

The second phase expands the course to a broader engineering cohort of approximately 25 students from mechanical, electrical, and biomedical backgrounds. This provides cross-disciplinary examples and highlights areas where course content requires adjustments. By incorporating feedback from engineers with varied experience, the curriculum progresses to emphasize transferable skills, such as prompt engineering, and integrating GenAI tools into traditional engineering work procedures.

In the third phase, the course moves online to provide instruction to 411 registered participants with 129 attending live, across different time zones and educational contexts. To address various needs, lessons are broken into 30–35 minute live demonstrations and video modules, daily participant surveys, peer-review assignments, presentation of student work, and live Q&A with open dialogue. These design choices maintain engagement and create a sense of community despite the lack of face-to-face interaction. Automated assessment tools provide immediate feedback, while weekly live tutoring hours offer real-time support and help to reinforce the key objective.

The final phase scales the course to 4,731 registered participants with 2,654 attending live on Day 1, requiring a strong infrastructure for community management, assessment, and course content updates. Modular curriculum design allows each lesson unit to be independent, so updates only affect the relevant module without changing the entire syllabus. Student-led feedback continues at scale, with discussion and chat forums and daily "teach outs" and presentation of participants' assignments, encouraging students to share new GenAI tool discoveries and suggest course improvements.

Throughout all stages, the curriculum focuses on universal principles and skills, such as responsible use, ethics, fairness, transparency, safety, and effective prompt engineering that apply across GenAI platforms. This combination of small-scale piloting, cross-disciplinary refinement, online engagement strategies, and modular updates provides a replicable framework for educators looking to grow GenAI programs from workshops to large-scale offerings. By documenting each iteration's outcomes and adaptations, this study prepares institutions with solid practices for maintaining curriculum relevance, providing learner autonomy, and managing complex operations as enrollment grows.

1.5.4 Broader Implications: Offers Transferable Insights for Education in Other Disruptive, Fast-Changing Domains

The methods used to teach this GenAI course can help educators in many fast-changing fields. By following these steps, teachers can build programs that stay up to date and work well for students and learners at any scale.

Modular design breaks a course into small, independent units. If a new prompt engineering technique appears, an instructor updates only that one lesson. If a new GenAI research tool is released, only the related module needs refreshing. This targeted updating means courses stay current and relevant without rewriting everything, saving time and reducing confusion for teachers and students.

Student feedback turns learners into active contributors. In class or during workshops, students might share links to the latest GenAI video tool or present a new feature in a model that was just released. By collecting real-world suggestions through surveys, forum posts, or written/audio/video reflections, instructors learn which topics matter most and can adjust lessons simultaneously. This practice not only keeps content relevant but also makes students feel ownership and agency of their learning.

Focusing on GenAI universal principles, such as responsible use, ethics, fairness, transparency and safety apply no matter which tool is being used. For example, learners need to understand how to effectively prompt the models, whether they use one application or the other. These skills and principles transfer across GenAI platforms. By teaching core methods instead of specific tools, students can adapt quickly when new applications and models become available.

This approach works whether you teach a small class or a massive online program. In a pilot of approximately 25 undergraduate students, instructors can provide detailed feedback in person. When the course expands to approximately 4,700 registered participants worldwide, instructors use a mix of peer reviews, surveys, and live support sessions. Short surveys check for basic understanding, while scheduled support sessions provide learners with scaffolded instruction. These different support systems help keep everyone engaged, no matter the course size.

These practices together create a repeatable model for any field facing accelerated change. Teachers begin with a small cohort to test core ideas, gather feedback from students, and update modules as needed. They then scale up to larger groups, using automated tools and peer support to maintain quality. By documenting each step, updates made, and feedback received, educators build a clear playbook. Other instructors can follow this guide to create flexible, inclusive, and effective courses that evolve alongside technology.