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Built on Teamed ResearchHow to Prove Your AI Skills as an Instructional Designer
A strong AI portfolio doesn’t just show the output. It shows the decisions behind it.
Key points
- 87.9% of AI-mentioning 2026 L&D job posts describe creating something with AI; 53.3% include responsible-use or checking language.
- “Prompt engineering” shows up in 15.9% of AI-mentioning candidate documents but only 6.7% of AI-mentioning job posts.
- Build one case study: what you made, where you intervened, and how you verified it.
A line on your résumé saying “prompt engineering” tells an employer very little about what you can actually produce. A finished learning asset, accompanied by the decisions that shaped it, tells a much richer story.
That distinction matters because employer language is increasingly focused on outcomes. In Teamed’s 2026 research, 87.9% of job postings that mention AI also describe creating something with it, and 53.3% include responsible-use or checking language.
At the same time, “prompt engineering” appears more often in AI-mentioning candidate documents than in AI-mentioning job postings: 15.9% (230 of 1,445) versus 6.7% (68 of 1,021).
The difference doesn’t make prompting unimportant. It suggests that the vocabulary professionals use to describe AI is not always the vocabulary employers use to describe work.
Show the work, then show your judgment
Choose one instructional design project and create a short case study with three components:
Show a lesson, storyboard, assessment, facilitator guide, video script or prototype. Explain the learning problem, the audience, and what AI contributed.
Identify the material you rewrote or rejected, the alternative approaches you considered, and why human judgment changed the result.
Describe your fact-checking, accessibility checks, subject-matter review, bias review, source attribution and intellectual-property decisions, as relevant to that project.
For example, a useful portfolio note might say:
“Developed a scenario-based compliance lesson using AI-assisted first drafts. Rewrote two scenarios after SME review, checked references against the approved policy, and tested answer feedback against the learning objectives.”
Sample portfolio note
Make the example safe to share. Do not upload client content, learner data, confidential policies or copyrighted materials without permission. Where necessary, create a fictionalized demonstration and label it as such.
Translate the artifact into résumé language
Instead of listing only a tool or “skilled at prompting,” describe the work and result:
Instead of Skilled at prompting
Write “Used AI-assisted drafting to develop a 12-module curriculum; documented SME and quality-review checkpoints.”
Instead of Prompt engineering
Write “Built a prototype learning activity with human-reviewed scenarios, feedback and accessibility checks.”
Instead of ChatGPT, Copilot
Write “Designed a repeatable process for reviewing AI-generated learning content before publication.”
Use only claims you can demonstrate. Don’t add invented productivity savings or imply that an AI-generated artifact was entirely your own manual work.
A recruiter’s perspective
At Teamed, we ask candidates for relevant work samples and organize links so hiring managers can assess work without hunting for it. That’s a practice-based observation, not a proven effect size. The research does not establish that linking a portfolio increases hiring success.
What the data does show is an emphasis on creating and checking AI-assisted work.
Percentages above describe text found in Teamed’s job-post and candidate-document samples. They do not measure demonstrated skill, interview practices or employment outcomes.
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