Every courseware project begins with good intentions—and an overwhelming amount of information. By the time you've collected SME interviews, existing content, policies, stakeholder feedback, competency frameworks, etc., you're already making compromises just to keep the project moving. You organize what you can, simplify where you must, and hope nothing important gets lost along the way.
With AI, that no longer needs to be the case. AI doesn’t create quality content faster, but it never gets overwhelmed by content. It can help track every requirement, cross-reference objectives and assessments, find contradictions, and remember decisions made weeks/months ago. It can maintain context far better than a human can (if trained correctly of course).
This lets instructional designers focus on judgment, facilitation, creativity, and collaboration. These are the aspects of the job I truly enjoy and where instructional designers have the greatest impact on learners.
How do we do this? How do we leverage the strengths of AI and instructional designers?
Building an Art Studio in a Factory
One word: artifacts.
Whether you call them Course Outlines, Assessment Strategies, Module Plans, or Design Briefs, artifacts have always been central to instructional design. At first glance, they look like standardized documents—the kind of repeatable outputs you'd expect from a factory. But they exist for a much more important reason.
Artifacts are how instructional designers make sense of complexity. We gather information from dozens of sources—SME interviews, existing content, policies, stakeholder feedback, learner needs, branding/writing/style guides—and distill it into documents that organize ideas, capture decisions, and create a shared understanding across the project.
Historically, those artifacts have also represented a compromise. We summarize information because we can't realistically hold every conversation, document, and requirement in our heads at once.
This is where AI fundamentally changes the process. Instead of replacing these artifacts, AI strengthens them. It can continuously analyze hundreds of pages of source material, maintain context across every document, identify inconsistencies, and trace decisions back to their origins. The artifact is no longer a simplified snapshot of the project—it becomes a living representation of it. Designers can continue collaborating, questioning, and refining ideas, confident that the supporting context hasn't been left behind.
My Factory and Art Studio
This isn't just a theoretical idea—it's a workflow I've started building into my own practice.
I've created agents that transform large volumes of source material into structured instructional design artifacts. One agent analyzes PowerPoints, Word documents, PDFs, and other reference materials to produce a Course Architecture document, complete with learning outcomes, module structure, key concepts, and mapped resources.
A second agent builds on that foundation by creating detailed module outlines that maintain alignment between objectives, assessments, and content while providing SMEs with clear guidance for content development.
The result isn't a finished course—it's a set of living artifacts that I can review, challenge, and refine with stakeholders. AI handles the complexity of organizing and maintaining context across countless inputs, while I focus on the conversations, decisions, and creative work that ultimately shapes the learning experience.
Connect with your institute's D2L Customer Success Manager or Client Sales Executive, or reach out to the D2L Sales Team for more information about how Learning Services can support you on your learning journey.
