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Built on Teamed ResearchDo Instructional Designers Need to Learn AI Engineering?
For most AI-mentioning L&D job postings, the emphasis is on applying AI to learning work, not engineering an AI system.
Key points
- 82.4% of AI-mentioning L&D postings ask you to apply AI to learning work. 11.8% ask you to build AI systems.
- Building courses or curricula is the most common work theme, at 28.8% of postings. AI tool building appears in 3.8%.
- Start with a familiar L&D problem: use AI, evaluate the result, improve it, and document what you did.
“Learn AI” can sound like “learn to code.” For an instructional designer deciding what to study next, that’s a consequential misunderstanding.
In Teamed’s 2026 analysis of job posts mentioning AI, the research classified AI language into three groups:
Literacy, curiosity and comfort
Create, design, prompt, review and teach
Agents, custom assistants, retrieval, fine-tuning and model work
What employers describe
Across all 2026 L&D job postings, Teamed found language about building courses or curricula in 28.8%, content creation in 26.3%, AI workflow integration in 25.6%, strategy and transformation in 22.8%, enabling others in 19.9%, and evaluating output in 19.3%. A narrower AI-tool-building theme appears in 3.8%.
The practical implication is not that technical AI work is unimportant. Roughly one in nine AI-mentioning postings fell into the study’s Build category. Those roles are real. But they should not define everyone’s learning plan.
A better starting point for practitioners
Choose a familiar L&D problem and use AI as part of a complete workflow. You might draft and refine a scenario, transform a SME interview into learning objectives, prototype assessment feedback, or produce a facilitator guide.
Then evaluate the result. Can you identify unsupported claims? Does the material match the audience and learning objectives? Are there accessibility, privacy or intellectual-property risks? Where does human review belong?
- 01Use
- 02Evaluate
- 03Improve
- 04Document
That sequence—use, evaluate, improve, document—is a more immediately relevant entry point for many L&D practitioners than starting with model architecture.
When engineering does make sense
Learn the technical stack if the work you want involves building custom assistants, integrating systems, setting up retrieval, or developing AI products. Those skills can be valuable, but they serve a more specific job objective.
Classification describes the wording of postings in Teamed’s dataset, not a representative measure of every L&D employer or an assessment of workers' proficiency.
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