AI in Instructional Design: How IDs Are Using AI in 2026

By Amy Vidor
Learning and Development Evangelist at Synthesia

Create engaging training videos in 160+ languages.

Every day, I'm inundated with hot takes about things AI is ruining. That list includes everything from the publishing industry and jigsaw puzzles to the job market. And yes, even learning.

If you're here because you're worried AI is going to make your job obsolete, I get it. But instructional design is a role that requires judgment. You bridge disciplines like learning science and psychology to decide what people need to learn, how to know if it worked, and what to change when it didn't.

AI doesn't know your organization or what good looks like. You do.

What AI can do is give you more time to focus on the parts that matter most, like measuring business impact. Here's how.

Key takeaways

• While AI can support every stage of ADDIE, how you use it and what you stay responsible for changes at each stage.
• Responsible AI use in instructional design requires governance. Standards, review, measurement decisions, and what counts as "good" still require human judgment.
• The biggest opportunity is in measurement. AI makes the loop shorter between delivering content and demonstrating its impact. That's the part of the job that has always mattered most and taken the longest.
• AI won't replace instructional designers. It will change where they spend their time.

What's changed for instructional designers

I used to lead a training where I'd ask participants to share a task they dreaded. For IDs, that list usually includes things like generating captions for a video training or analyzing thousands of survey responses for emerging patterns.

Thanks to AI, those tasks have gotten faster or even automated. That capacity adds up, and the best use of it is strategic work.

Where AI adoption is strongest today

87% of learning and development practitioners reported using AI in our 2026 AI in L&D Report. Most of that usage concentrates on content creation: tasks like quiz and content drafting, video creation, and translation.

Even though adoption is broad, it is uneven in maturity and distribution across instructional design workflows. The chart below shows where adoption is strongest across the ADDIE model.

An important caveat about our data. Many of the respondents are customers or practitioners in our networks. As an AI company, this likely skews the levels of AI engagement reported. Of 421 respondents, 38% were instructional designers.

6 ways instructional designers are using AI

If you're looking for inspiration on how to bring AI into your L&D workflows, here's what I recommend. Start with tasks where there's a clear input and output and the risk of failure is low. Something like generating wrong answers for an assessment. If it messes up, you can easily correct it. No big deal.

1. Transcribe and synthesize stakeholder interviews
   Record your calls with videoconferencing software so you can easily upload transcripts and ask AI to pull out recurring themes or potential gaps.

2. Surface performance gaps
   If you have performance data from annual reviews or probation reviews, ask AI to find patterns of performance issues.

3. Draft learning objectives
   There comes a point where you've exhausted Bloom's taxonomy of verbs and are feeling uninspired. Bounce ideas around in a chat to cut through writer's block.

4. Generate discussion prompts and learning activities
   If you're designing a live session, you may want more thought-provoking questions or if the same session runs multiple times, a variety of those prompts.

5. Build assessments and rubrics
   Whether you need knowledge checks sprinkled throughout a course or a rigorous certification assessment, have AI help propose questions and distractor answers.

6. Produce content assets
   The development stage is where most IDs start with AI. This is where the volume of work is highest and probably where you can use the most support.

Benefits and challenges of using AI in instructional design

Content production

Benefits: AI helps IDs create content more quickly. 88% of respondents to our 2026 AI in L&D Report reported saving time on content creation.

Challenges: But creating content faster doesn't mean it's working. I've described this pattern as readiness debt. We're producing more, but not following through to understand whether that content is leading to better outcomes.

Cost

Benefits: Using AI is correlated with cost savings for IDs. If you're able to outsource less work, say to a graphic designer or video production company, you're potentially saving thousands of dollars. 45% of teams are already reporting financial benefits, though many expect clearer gains as AI becomes more embedded in their workflows.

Challenges: While AI can reduce operational overhead, AI tools aren't free. Enterprise licensing, usage caps, and the cost of more powerful models for complex tasks all add up.

Feedback loops

Benefits: AI makes it easier to collect and act on evaluation data, shortening the loop between delivering content and understanding whether it's working. For a full framework on how to do this, see the measurement section below.

Challenges: The gap between content creation and evaluation is growing. Only 19% of L&D teams are using AI for evaluation, compared to 65% using it for content development. We're creating faster but not following through.

Skills and capacity

Benefits: AI can exponentially increase the impact of your skill set. You can learn new tools faster, build your own automated workflows, and take on work that previously required specialists.

Challenges: But there's a real risk of workload creep. Research from UC Berkeley Haas found that AI didn't reduce work, it intensified it.

Business impact

Benefits: 41% of L&D teams say AI is already contributing to business impact through faster delivery and higher output.

Challenges: Most teams haven't reached the maturity to demonstrate measurable business impact from AI in ID. Only 9% have reached the stage of scaling AI across their organization, and just 6% describe AI as fully integrated.

AI tools for instructional designers

When considering which AI tools to use, whether for the examples above or another task, I recommend starting with an assessment of your existing tech stack.

Example use case: training videos

Say you're looking to update or build a video content library. You have existing materials, perhaps a script, slides, or even a webinar transcript. With an AI video tool like Synthesia, you can transform that source material into a first draft in minutes.

How to use AI to measure whether learning is working

If you try out any of these use cases, one of the benefits should be getting some time back in your day. I'd recommend using that freed up capacity to focus on measurement.

Key definitions

• Formative evaluation is continuous evaluation conducted throughout the design and development process.
• Summative evaluation is conducted after training to measure impact and efficacy.

Where you need a human in the loop

While AI accelerates production and supports intelligence, L&D teams continue to own learning science, contextual judgement, ethical standards, quality assurance and brand voice — keeping humans firmly in control of relevance and rigor. — Dr. Philippa Hardman

What the future of instructional design looks like

The role of instructional design is evolving. More of the production work will happen through AI. The responsibility for design and evaluation will stay with instructional designers.