Embracing AI in Course Design – Insights from Creating the ‘Principles of Responsible Management’ Course
In an exciting new project at Learning Design Solutions, we embarked on a journey to harness AI for designing a fully online, university-level course titled Principles of Responsible Management. This course, aimed at Master’s students, spanned four weeks and was structured for 5 UK academic credits (CAT points), equating to about 50 hours of student engagement. With AI at the core of the design process, we aimed not only to streamline the course development timeline but to uphold robust educational standards rooted in learning science and sound pedagogy. Here, I’d like to share some reflections on the process and what we learned along the way.
If you are interested in viewing the course, visit: Learning Design Solutions Moodle Cloud
Integrating Human and AI Expertise for Effective Design
The project’s key takeaway was that AI can indeed play a powerful role in expediting course creation, but successful outcomes require an integrated approach between human and machine. The course was shaped through a collaborative model that brought together myself, Andrew Doig as Learning Designer (LD), Faye Taylor as Subject Matter Expert (SME), and Steve Hogg as Learning Technologist (LT). Our AI partner, a ChatGPT bot we’ve named the ‘Learning Design Expert,’ had been specifically trained to recognise principles of effective course design and learning science.
Faster Course Design without Compromising Quality
Using AI significantly reduced the time required to build this course. Instead of developing content from scratch, we leveraged the bot to draft content for each module based on an outline we provided. Working closely with Faye, our SME, we mapped out learning outcomes and performance indicators, ensuring they were spread appropriately across four weeks. This was no plug-and-play solution; while AI provided a foundational draft, every output required human refinement to ensure it met our pedagogical standards and was aligned with assessment and learning outcomes.
A Guided, Iterative Process with AI
Throughout, our approach with the AI was iterative. After outlining learning outcomes, we provided the AI with specifications to create assessment briefs and grading criteria. By feeding it prompts based on these specifications, we iteratively guided the bot’s outputs to shape content for weekly modules, assessments, and interactive activities. At each stage, we critically evaluated and adjusted its drafts, especially when scripts or quizzes needed refining for clarity or depth. For instance, the AI sometimes needed further instruction on the structure of narrative or content progression typical in asynchronous online learning. This guidance helped avoid over-simplification of complex topics. We also successfully guided the bot to adding citation and reference wherever possible to appropriately academic texts – something that we definitely needed Faye’s input to cross check.
Designing in Stages: Ensuring Pedagogical Integrity
The storyboard phase was particularly interesting, as we used the bot to write detailed weekly storyboards. The process wasn’t linear; the AI required guidance to draft tasks and scripts, and it sometimes diverged from the intended structure or added elements we hadn’t specified. This staged approach—writing introductions, scripts, quizzes, and case studies separately—allowed us to manage the bot’s tendency to produce content in unexpected ways, ensuring each element remained pedagogically sound. The AI’s ability to rapidly prototype enabled us to generate, critique, and refine, moving progressively from week one to four with an expanding set of exemplars.
Building and Finalising the Course with a Learning Technologist
Once the storyboards were completed, we collaborated with Steve Hogg, our Learning Technologist, who expertly translated the content into Moodle and Articulate Rise or Storyline elements. Ultimately, Steve’s role remained much the same as it would in a non-AI project. However, in the writing stage, we were able to encourage the AI to write for the medium – text for on-screen, scripts for videos with prompts for visuals, storyboards for Rise or Storyline content – making Steve’s strategy for build more straightforward.
Reflections on AI in Course Design
AI Accelerates but Does Not Replace Human Expertise: While AI drastically cuts development time, it does not eliminate the need for expert input. An experienced learning designer’s role is still essential to guide the AI, ensure quality, and maintain a coherent, engaging course structure.
Continuous Collaboration with SMEs: The AI could not fully comprehend the academic nuances and standards required at a university level. Our SME’s involvement was critical in refining the AI’s outputs and ensuring content accuracy.
Adapting Assessment for AI-Enhanced Learning: To minimise academic misconduct and encourage authentic engagement, we integrated AI-aware assessment strategies. These involved open-ended, reflective tasks and scenario-based assessments, making it challenging for students to rely solely on AI for answers.
Final Thoughts
Our Principles of Responsible Management course will soon be available, and we are excited to release it as a model of human-AI collaboration in online learning design. This project has shown that, with careful planning, AI can serve as an invaluable asset in educational course creation. However, achieving the high standards expected in higher education still requires human insight, pedagogical knowledge, and collaborative effort across roles.
AI in course design is not a shortcut to quality but a powerful tool when harnessed effectively. As technology evolves, we look forward to seeing how AI can continue to enhance our educational practice, driving innovation while preserving the educational values that remain fundamental to effective learning.