One tool among many

AI-Enhanced Learning Design

AI is a tool that supports Kirsten's instructional design process by helping accelerate research, brainstorming, prototyping, content organization, and workflow improvement. Human understanding, instructional strategy, and learner needs remain at the center of every design decision.

A designer reviewing a printed draft beside a laptop — AI drafts. A designer decides what learners actually need.

Analysis

Understand faster, decide the same way.

AI-assisted

Research synthesis across scattered source material and prior program documentation.
Audience analysis drafts — likely prior knowledge, obstacles, and motivations to verify with real people.
Content review that surfaces gaps, contradictions, and unstated assumptions in SME material.

Stays human

Deciding whether learning is the right intervention at all, and which performance gap is worth solving first. AI can summarize a problem; it cannot own the recommendation.

Design

More options on the table, chosen by a designer.

AI-assisted

Brainstorming instructional approaches and alternate sequences to compare against each other.
Scenario creation — generating plausible situations and wrong answers, then hardening them against real practice.
Storyboarding drafts that make structure visible early enough to change cheaply.

Stays human

Objectives, assessment evidence, and alignment. If the objective and the evidence are wrong, an efficiently produced course simply arrives at the wrong destination faster.

Development

Build the prototype while the thinking is still warm.

AI-assisted

Working prototypes learners and stakeholders can click, instead of static specifications.
Content refinement for plain language, reading level, and consistent voice.
Accessibility checks on structure, alt text, contrast, and keyboard operability as a first pass.

Stays human

Instructional accuracy, tone with a specific audience, and the final accessibility judgment. Automated checks catch defects; they do not confirm the experience is usable.

Evaluation

Read the signal, then decide what changes.

AI-assisted

Feedback analysis across open-response comments to surface recurring themes.
Pattern detection in assessment data to locate where learners consistently break down.
Improvement recommendations as a first draft for the next iteration.

Stays human

Interpreting whether the original performance gap actually closed, and choosing which changes are worth the cost of making them.

Working rules

Four constraints applied to every AI-assisted project

Judgment is not delegated

Analysis, objectives, assessment alignment, and accessibility decisions stay with the designer. Those choices determine whether learning works.

Everything factual is verified

AI output is treated as a draft, never as a source. Any claim, figure, or citation is confirmed before it reaches a learner.

Data minimization by default

Confidential, proprietary, and personal information is never pasted into a tool that has not been approved for it.

Speed is spent on quality

Time saved on drafting is reinvested in analysis, practice design, and revision — not in producing more content.

Designing learning for new tools and ideas

These two demonstrations are not courses about AI so much as courses about adoption — building learner confidence, responsible use, and practical application when something unfamiliar lands on people's desks.

AI Workplace Essentials AI Productivity Toolkit