How I Used AI to Speed Up Micro-Course Production (AI Assists, but YOU Decide)

AI gets talked about a lot in instructional design circles. Most of that conversation stays at the surface:  "it can write scripts" or "it saves time" or maybe even “It’s taking our jobs!” My view is that AI is an assistant or a tool that a working ID can integrate AI into a real production workflow without handing over creative control or ending up with generic, ineffective content. Here is a little insight into my process.

Break production into discrete tasks

I think one of the biggest mistakes ID’s make is asking AI to "create a course." That's not a prompt; it's a project brief. I think a better approach is treating every deliverable as its own conversational task.

Over the course of production, I broke the work into individual tasks:

• Course descriptions and objectives

• Audience statements

• Voiceover scripts with specific timing targets and tone directions

• Video opener and summary plans with scene-by-scene breakdowns

• Persona and character development

• Vignette scripts with dialogue

• Knowledge check questions by format

• Reflection prompts

• Glossary terms and definitions

• Formatted Word and Excel exports

Each task got its own prompt. Each prompt included the constraints that help to ensure the output is not vague and generic: word count, timing, tone, format, what to include, and what to avoid.

Use constraints as prompts

Telling AI what not to do is as important as telling it what to do. Create some parameters and constraints and use those in your prompts too. My constraints usually focus on things like tone, timing, format, and scope.

Some examples:

• Tone: "Write like a real person. Casual and direct. No filler phrases like 'it's important to note' or 'delve.'"

• Timing: "This script should be 60–90 seconds. Verify the word count."

• Format: "No colors or special formatting. No chapter references."

• Scope: "Only include terms unique to this chapter. Exclude anything already covered in previous glossaries."

I find that this approach means far less editing for me before I can use the output in an actual course or as a learning aid.

Iterate in conversation

Don’t be afraid to iterate. Draft, correct, refine, and even set a new direction if needed. The conversation itself becomes part of the production process.

Here are a few suggestions:

• Ask for options, then decide. "Riff on a few titles" or "suggest three alternate endings" generates a range quickly. Picking from a set is faster than generating from scratch over and over.

• Give specific feedback. "Shorten this question by five words" is more useful than "make it shorter."

• Ask for verification. "Please provide a word count and timing estimate" after every script kept length on target without guessing.

• Identify what's working. Saying something like "keep the tone, adjust the length" preserves what's good while fixing what isn't.

Maintain your voice through clear style standards

AI will drift toward generic if you let it. It’s the reason you are probably getting pretty good at identifying AI generated text. Keeping a consistent voice across dozens of deliverables requires stating style expectations clearly and repeating them when needed.

• Casual, direct, no corporate-speak

• No preambles; get to the point

• Write like a person, not a policy document

Stating these expectations upfront, then and reinforcing them when a draft missed the mark meant each revision moved me closer to the target.

Use AI to produce where it makes sense

My workflow did sometimes go farther, not just creating drafts but using AI to generate (mostly) production-ready outputs. Pro-tip: don’t put anything into production, or pass on anything to a colleague, you have not reviewed and verified.

Here are some things I generated:

• Branded Word documents with consistent formatting

• Excel glossaries ready for system upload

• Scene-by-scene video plans with production notes

• Complete vignette scripts with dialogue and stage directions

AI assists, but YOU decide.

This is the part that gets glossed over in most AI workflow posts, so let's be direct about it: every single output requires human review.

Scripts got read aloud to check timing and flow. Knowledge check questions got evaluated for accuracy and distractor quality. Glossary definitions got verified against the source material. Video concepts got assessed for instructional fit. Reflection prompts got read through the lens of the actual learner, not the AI's interpretation of one.

AI generated the drafts. A human with subject matter expertise, instructional design judgment, and knowledge of the audience made the calls about what stayed, what changed, and what got cut entirely. Those are not the same thing and confusing them is how you end up with content that sounds fine but doesn't actually meet the needs of your learner.

The workflow is faster because AI handles the production heavy lifting. The final product is to spec, addresses real-learning needs, prompts changes in behavior and skill, and prepares students for assessments because a human has verified, edited, and validated all the AI work.

There is a lot of fear in the ID community that AI is replacing instructional designers. In my view, used as a tool, AI accelerates certain parts of the process, allowing ID’s to focus on the parts that really matter. Smart designers (and smart companies!) know ID knowledge, skill, and point-of-view are a vital part of the process of creating real, impactful learning experiences.