Jeffrey Joiner — Instructional Designer

I've spent the last year deliberately testing AI tools in my instructional design practice — not because I was told to, but because my faculty are asking, and I'd rather have an informed perspective than a borrowed one.

Here's what I've found actually useful, what's still frustrating, and what I think the longer-term implications are for our field.

Where AI Has Actually Saved Me Time

First-draft generation for learning objectives. I give an AI model my course description, level, and discipline, ask it to draft measurable learning objectives aligned to Bloom's cognitive taxonomy, and use the output as a starting point for conversation with the faculty member. Even mediocre AI output gives us something concrete to react to, which is often faster than starting from a blank page together.

Accessibility remediation. Using AI-assisted tools to generate alt-text for images and first drafts of captions has meaningfully reduced the time our team spends on accessibility work. The outputs still need human review — AI consistently struggles with data visualizations and discipline-specific diagrams — but the effort has shifted from creation to editing.

Scenario-based content generation. For developing case studies and branching scenarios, especially in healthcare and business programs, AI can generate plausible scenario seeds quickly. I've used this to give faculty five different scenario variations to evaluate, which surfaces their preferences and disciplinary assumptions in ways that free-form brainstorming often doesn't.

Where It's Still Frustrating

Assessment design. AI-generated quiz questions are mostly surface-level recall. Generating items that assess genuine application or analysis — the stuff that matters — still requires significant human craft and disciplinary knowledge. I've stopped hoping AI will fix this and started treating it as a generator of raw material that needs heavy editing.

Anything requiring nuanced institutional context. AI doesn't know that your first-year composition students have already taken a data literacy requirement, or that your nursing program's accreditor has specific requirements about how learning outcomes must be phrased. The instructional designer's judgment is still the essential ingredient for work that has to fit a specific context.

What I Think This Means for Our Roles

The instructional designers who will find AI most useful are those who already have strong pedagogical frameworks — because they'll know what good AI output looks like and what needs to be changed. The risk is that AI makes it easier to produce-and-deploy mediocre instructional materials faster. Our value-add increasingly lives in the quality gatekeeping and strategic thinking that AI can't yet replicate.

That's not a comfortable story, but I think it's an honest one.