Engaging with topic 4 designing online and blended learning has encouraged me to rethink how I approach course design, especially as I transition one of my traditionally delivered electrical-engineering courses into a blended format. The discussions around active learning, student engagement, and the thoughtful balance of synchronous and asynchronous activities helped me realize that effective online/blended design is not just a matter of transferring existing content to digital platforms. Rather, it requires intentional planning, a clear pedagogical foundation, and a recognition that online presence, structure, and community-building are just as important as subject-matter expertise. As such, how much different a course design would appear if the course developer relied on an AI tool as a key-enabler in pedagogical course development? In simple words, will AI produce a better output including the latest tools / technologies / outputs; Or will it miss out on certain deeper level logic and pedagogical approaches; Could AI be used to “enhance” course creation/as a co-creator? To explore this, I began with an established syllabus rooted in conventional pedagogical pathways—weekly lectures, a written exam, and laboratory sessions— and I engaged ChatGPT as AI tool aiming to compare the outcomes.
Working with generative AI as part of the design process has been particularly illuminating. The AI provided structured, coherent, and visually appealing outputs—including course maps, activity types, technology suggestions, and redesigned assessments. It rapidly assembled a diverse set of modern tools and strategies that could enrich the learner experience, demonstrating how AI can accelerate idea generation and broaden the design space. However, while the AI-generated plan was attractive and comprehensive, I also became aware of its limits. Topic-specific tools, disciplinary nuances, engineering-specific practices, and the contextual realities of my teaching environment still require human judgment. My own experience, preferences, and objectives as a course developer must shape which tools are actually appropriate, feasible, and pedagogically meaningful for my learners.
This also applies to pedagogy. AI can support the development of pedagogically sound structures—aligning learning outcomes, promoting interactivity, and suggesting active learning strategies—but it cannot fully capture the deeper logic, constraints, and subtle decisions that arise in real teaching contexts. Course design unfolds within a dynamic educational environment, influenced by student readiness, institutional culture, assessment policies, and real-time classroom interactions. The richer pedagogical reasoning—what to emphasize, when to scaffold, how to respond to emerging challenges—must still be refined by the educator. Thus, while AI is a valuable assistant, the responsibility for creating a coherent, authentic, and educationally responsible learning experience ultimately rests with the course developer.
Overall, this topic has strengthened my understanding that designing for online and blended learning is both a creative and reflective process. AI can certainly enhance efficiency and innovation, but it should inform rather than drive pedagogical decisions. As educators, we need to combine the speed and breadth of AI-generated support with our own contextualized judgment to create learning environments that foster engagement, critical thinking, and meaningful student development.
Andreas
The prompts and the original syllabus are available upon request.
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