About a year ago, I was supporting a large faculty-led course redesign at the university where I was hired as a contract-based instructional designer. It was a general education course that enrolled several hundred students each semester and hadn’t been significantly updated in years. The faculty member I was partnering with was extremely knowledgeable and genuinely invested in improving the student experience, but our collaboration time was limited to a few hours a week between her teaching load and committee obligations. Meanwhile, I was responsible for rebuilding the course from the ground up: rewriting learning objectives, creating effective activities, developing assessments, and preparing materials for review. All of that needed to be done on an academic calendar that doesn’t pause for anyone.
I was spending the majority of my time on production tasks. The higher-order work such as the learning architecture, the alignment between course outcomes and program goals, the decisions about how students would actually engage with the material, kept getting compressed because there was always another deliverable waiting.
That project pushed me to seriously explore AI tools, and what I discovered fundamentally changed how I approach course development. Not because the tools are perfect, they are not! They require careful review and professional judgment at every step. But because they gave me something back that I hadn’t realized I’d lost: the capacity to focus on the work that actually requires a trained instructional designer.
That’s the real value of AI in instructional design. Not speed for its own sake, but the ability to redirect your expertise toward the decisions that matter most, while the tools handle the production work that was quietly consuming your day.
Here’s a look at the tools I’ve found useful and how they fit into a professional workflow.
ChatGPT: A Thinking Partner That’s Always Available
ChatGPT has become the most consistently useful tool in my practice, not because it writes courses for me, but because it accelerates the brainstorming process at every stage of a project.
Writing learning objectives is a good example. Even though it’s a straightforward task, it often takes longer than it should, especially when you’re working across multiple modules and trying to maintain alignment with Bloom’s Taxonomy throughout. A prompt like “Generate five measurable learning objectives for an introductory research methods course, aligned to Bloom’s Taxonomy”, produces a working draft in seconds. To reiterate, it won’t give me a final version, but rather a version I can evaluate, refine, and build on rather than construct from nothing.
The same principle applies to learning activity design. When working with faculty, I often need to present a range of pedagogically sound options quickly, particularly for instructors who are new to active learning approaches. ChatGPT consistently surfaces ideas worth discussing like case-based analysis activities, structured peer review prompts, scenario-driven problem sets that connect course content to real-world application. Even when the suggestions require significant refinement, they give faculty something concrete to react to, which makes collaborative conversations far more productive.
Additionally, for assessment development, particularly scenario-based or application-level questions, the tool produces drafts that, with careful editing and faculty review, are strong enough to be genuinely useful. Obviously, the output requires a trained eye to evaluate, but it’s a far better starting point than a blank page.
AI Image Generation: Purpose-Built Visuals Without the Search
Instructional designers are not typically graphic designers, and the gap between “the image I need” and “the image available in a stock library” can be surprisingly wide. AI image tools like Midjourney and DALL-E close that gap considerably.
When developing a public health course that needed visuals depicting community health scenarios like a nurse conducting an outreach visit, a student practicing a clinical assessment, I could generate contextually accurate images in under a minute, styled consistently and specific to the instructional context. Stock photography rarely delivers that level of specificity, and when it does, it often carries visual cues such as overly polished settings, and clearly corporate aesthetics, that undercut the authenticity of the scenario.
For scenario-based learning, where contextual realism matters, the ability to generate visuals that match your instructional narrative rather than the other way around has a real impact on how credible and engaging the learning experience actually feels.
AI Presentation Tools: From Blank Slide to Structured Prototype
Slide deck development is one of those tasks that can easily absorb hours that should be spent on higher-order design work. For me, Gamma has become a reliable tool for compressing that timeline. Given a topic and a rough scope, it generates a coherent slide structure with content suggestions and visual placeholders, which gives you a working skeleton to refine rather than build from scratch.
This is particularly useful when working with faculty who have provided detailed notes or lecture content but need help translating that material into an instructional format. When source material already exists, Gamma can transform it into a structured visual presentation in minutes. This is a meaningful efficiency when the semester start date is fixed and there’s no flexibility in the timeline!
Another tool that recently earned a place in my workflow is Manus. Unlike tools that function purely as text generators, Manus can handle multi-step tasks, making it well-suited for course development work. It can also generate full presentation outlines that are logical, coherent, and ready to import into PowerPoint or an authoring tool, which is valuable when moving quickly from concept to a reviewable draft.
AI Video Tools: Reducing the Production Barrier
All instructional designers know that video is one of the most resource-intensive elements of course development.However, Synthesia changes that significantly. The workflow is straightforward: you write a script, select an AI avatar and voice, and generate the video. For content like course orientation materials, module introductions, or procedural walkthroughs, the output is professional and does not require studio time, recording equipment, or significant post-production effort.
Descript addresses a different part of the video workflow, which is editing. Its core functionality, editing video by editing the transcript, sounds simple, but the practical impact is significant. For faculty who record their own lecture content, or for instructional designers managing video-heavy course builds, the reduction in editing time is immediate.
AI Voice Tools: Professional Narration at a Realistic Cost
High-quality professional voice talent remains the gold standard for polished audio in online courses. However, it, unfortunately, comes with timelines and costs that are rarely available in academic course development budgets.
ElevenLabs produces AI-generated narration that is natural, clear, and a significant improvement over the text-to-speech tools most instructional designers have encountered. Here’s my typical workflow: I finalize the narration script, select a voice that suits the course tone, and export the audio. For module narration, scenario-based dialogue, and asynchronous instructional content, the quality is well within the range of professional acceptability. For early-stage course prototypes, it allows you to present a fully realized experience to faculty and curriculum committees without waiting on audio production.
Rapid Prototyping: The Cumulative Impact
The most significant shift I’ve experienced since integrating these tools isn’t any single capability, but rather what happens when you use them in combination. A prototyping workflow that once took the better part of two weeks can now be completed in a day:
- ChatGPT for learning objectives and content structure
- Manus or Gamma for slide decks and course framework
- Midjourney or DALL-E for contextual visuals
- ElevenLabs for narration audio
In a university context, where faculty review cycles and academic calendars are fixed constraints, this kind of speed has real strategic value. Arriving at a curriculum committee meeting or a faculty consultation with a working prototype rather than a concept document changes the quality of the conversation entirely. Feedback becomes specific and actionable. The design process gains momentum earlier, and decisions that might otherwise wait until the next meeting cycle can be resolved on the spot.
What AI Doesn’t Change
While AI tools can be very helpful and can save significant time for instructional designers, there are also many things these tools cannot do. We know that AI tools can generate content, visuals, and media efficiently. However, they do not make the decisions that define the quality of a learning experience such as aligning course outcomes to program and accreditation goals, designing assessments that measure meaningful learning, applying evidence-based principles to sequencing and instructional strategy, or understanding the specific needs and contexts of your student population.
Those decisions require professional expertise that AI cannot replicate. The instructional designers who will be most effective in the years ahead are not those who resist these tools or those who defer entirely to them, but those who have a clear sense of where AI adds value and where their own professional judgment remains irreplaceable. In other words, the production work is increasingly something AI can handle well, but the learning design is still yours.
Want to create high-impact course content in half the time without the overwhelm? My AI Toolkit for Course Creators includes detailed prompts to help you plan, structure, and script your next online course.
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