AI-Powered Development & Implementation
Five Use Cases to Improve the Quality & Speed of the Build Stages of ADDIE
Hey folks!
This week on the AI & Learning Design Bootcamp, we’re diving into AI-Powered Development and Implementation.
The question we’re exploring this week is: how does AI impact the speed and - critically - the quality of the development and implementation stages of ADDIE?
Here’s our emerging list of the most powerful tried and test AI use cases for you to try.
Let’s go! 🚀
Use Case 1: Select a Scripting Strategy
Ask research tools like Consensus, Perplexity and STORM: what are the most effective instructional scripting strategies for X content type [e.g. video] for X learner [insert learner profile] to help them to achieve Y learning objective [insert objective]? Use the results to define the optimal way to write instructional scripts for your course.
💨 Speed Impact: Significantly faster research process, reducing discovery time from days to minutes by instantly synthesising then summarising instructional design best practices.
📈 Quality Impact: Significantly higher-quality scripting strategies based on research rather than individual opinion or habit. Provides evidence-backed approaches tailored to specific content types, learner profiles and goals.
Use Case 2: Write Instructional Scripts
Give Co-Pilot, Claude or ChatGPT 4o a course outline with a summary of your learning objectives and a description of the content & activity you will use to achieve each objective. Then, ask AI to create instructional scripts for separate elements of each module (intro text, video, activity etc) using the strategy or strategies surfaced in Use Case 1.
💨 Speed Impact: Dramatically reduces script creation time from days to hours/minutes. Eliminates blank page syndrome and creates draft content immediately.
📈 Quality Impact: Significant, but only if you apply best practices (i.e. tell AI both what to do and HOW to do it), following guidelines from Use Case 1. Initial drafts may lack authenticity without refinement and can feel generic without personalisation (see Use Case 3!).
Use Case 3: Create a Style Guide & “Humanise” Scripts
To “humanise” and personalise AI-generated scripts, find 3-5 samples of writing e.g. blog posts or org docs. Share these one by one with Claude or ChatGPT 4o and task instruct them to learn how to emulate the desired style, e.g. tone of voice, length, structure etc. To do this, ask AI to use the samples to create a style guide, then use this as an input for AI to follow when it edits or writes scripts.
💨 Speed Impact: Fast creation of comprehensive style guides and efficient script editing, cutting editing time by 50-70%.
📈 Quality Impact: Significant quality improvement through consistent voice, personalisation, and brand alignment. Helps AI-generated content feel more authentic and tailored to specific audiences while maintaining instructional effectiveness.
Use Case 4: Quality Assurance
AI is pretty good at comparing text-based outputs. Give Claude or ChatGPT 4o a copy of a rubric, e.g. a set of quality standards & accessibility standards. Ask AI to read the rubric carefully and “explain it back”.
Once it provides a reliable summary, give AI a design outline or storyboard and tell it to assess it against the standards provided. It works well when you ask it to a) assess how much the design aligns with each of the standards and b) suggest recommended changes required to meet the standards fully. Work standard by standard to ensure focus and elevate the quality of its responses.
💨 Speed Impact: Automates comprehensive quality checks that would take days to perform manually, providing instant feedback on multiple quality dimensions simultaneously and consistently.
📈 Quality Impact: More thorough and consistent quality assessment against established standards. Identifies issues humans might miss, especially in areas like accessibility, consistency across materials, and alignment with rubrics.
Use Case 5: Rapid Prototyping
Until now, prototyping and iteration has been a nice to have but almost impossible to execute task in the development and implementation stage of ADDIE. Thanks to AI, this is changing. For example, try using an AI prototype tool like Vo by Vercel to rapidly build out a working prototype of one or more online course modules quickly and gather feedback through rapid testing.
💨 Speed Impact: Transforms prototyping from weeks of development to minutes, enabling rapid creation, testing and iteration of designs.
📈 Quality Impact: Enables practical testing and validation before full implementation, reducing revision cycles. Allows for evidence-based refinements based on actual user interaction rather than theoretical assumptions.
Here’s a five min demo that I made for the class about how Vercel works in practice:
That’s all folks! If you have any additional AI use cases for the Development & Implementation part of the process, I’d love to hear about them!
Happy innovating,
Phil 👋
PS: If you want to experiment with AI supported by me and a cohort of educators and instructional designers like you, check out my AI Learning Design Bootcamp.