Smarter Course Design With AI – 5 Proven Use Cases

“AI won’t replace you, but an instructional designer who uses it wisely just might.” That’s the mindset e-learning professionals are adopting in 2025.

To explore where AI fits into modern course development and how experienced IDs are using it to support their daily workflows, we spoke with Anna Poli, a senior instructional designer at iSpring.

5 Practical Ways to Use AI With Instructional Design

AI in learning and development has generated considerable hype, but it has not fulfilled every promise to instructional designers. It didn’t turn SMEs into content geniuses, fully automate course creation, or replace the need for instructional judgment and the human touch.

Still, when used strategically, it’s a powerful assistant, saving time, reducing repetitive tasks, and creating space for deeper design work. Here are five areas where it can make a real difference:

1. Early-Stage Course Prototyping

Instead of starting with a blank slide, AI helps generate a rough outline or suggest slide content based on a topic, script, or meeting notes.

“I enjoy using Copilot in PowerPoint paired with iSpring Suite Max as an add-on. It lets me build a course flow from scratch and enhance it with interactivity: quizzes, branching scenarios, or video narration,” says Anna Poli.

AI-supported prototyping helps instructional designers test ideas, share early drafts with stakeholders, and refine structure and flow before investing time in full-scale production.

2. Training Content Repurposing

For senior instructional designers managing large content ecosystems, AI course creators offer a practical way to develop and adapt courses without starting from scratch.

Long-form assets, such as webinars, manuals, or SME recordings, can be transcribed, summarized, and restructured into self-paced modules, interactive microlearning, or quizzes within minutes.

With AI, it’s also easier to maintain consistency across formats, identify gaps or redundancies, and adapt content to different roles or learner levels.

3. Localizing Content for Global Teams

When rolling out training across multiple regions, AI-powered translation tools like DeepL or built-in features in authoring platforms can translate course scripts, assessments, and UI elements with a few clicks, providing IDs with a strong draft before human review.

“I usually start with AI-generated translations to get the base version out quickly,” says Anna Poli. “Then, I loop in regional reviewers for cultural nuance and terminology. It saves hours of back-and-forth.”

4. Enhancing Accessibility

Accessibility is about giving every learner a fair opportunity to engage, absorb, and apply what they’re learning. But in large-scale learning projects, this is often treated as an afterthought, when time and resources are running low. AI helps shift that work to an earlier point in the process. Instructional designers can:

• Generate voiceovers using text-to-speech tools while content is being finalized.

• Add auto-captioning to training videos, making them easier to follow for learners watching without sound.

• Draft alt text for visuals, diagrams, and screenshots, which can then be quickly refined for context.

• Adapt reading-level complexity, making content clearer for diverse learner groups.

5. Generating Knowledge Checks

Good assessment questions take time to develop, especially when you need to challenge learners at different levels of Bloom’s taxonomy. AI helps by generating draft questions from raw content, such as course modules, transcripts, or SME notes.

“Instead of staring at a blank page, I use AI to get five or six rough questions, then fine-tune them based on the learning goals and audience,” says Anna Poli. “These drafts often need refinement, but they provide a starting point that speeds up the process greatly.”

Where to Draw the Line: What AI Can’t (and Shouldn’t) Do

While AI can support many aspects of course development, there are still areas where AI should not be trusted to work solo, even if it seems tempting.

To make your teamwork more meaningful, don’t dump these (still very human!) tasks on your AI friend:

• Define learning objectives without stakeholder input or performance context.

• Generate culturally sensitive examples or scenarios without review.

• Replace SME conversations, especially in high-risk or regulated fields.

• Design ethical decision-making scenarios without nuanced judgment.

• Catch subtle tone issues in language that could alienate learners.

Using AI as a collaborator, rather than a competitor, will definitely give you an advantage. While some teams are still cautious about adopting AI, those who use it thoughtfully are already gaining a strategic edge—and more time for big-picture thinking.