Problem

GED learners often have to piece together study resources, track progress, understand tested skills, and decide what to study next across multiple websites and tools.

Solution

I designed a three-month, subject-specific GED Study Planner prototype that brings study planning, progress tracking, tested skills, formulas, and curated learning resources into one experience.

My Role

I defined the learner need, designed the experience and information architecture, selected and organized learning resources, developed the prototype, and tested/refined functionality.

AI Use 

I was able to use skills developed through the Google AI Professional Certificate and Google AI Studio to turn the concept into a functional prototype through conversational development: prompting, testing, refining functionality, and troubleshooting as the application evolved. 

Outcome 

I created a functional prototype where learners can select a GED subject, follow a three-month study plan, access relevant YouTube/Khan Academy resources, review tested skills and math formulas, record study time and milestones, track practice and official test scores, and access GED Ready/Kaplan resources.

Challenge 

Building a functional application introduced a different kind of iteration than traditional eLearning development. Some features still have bugs, making testing, troubleshooting, and prioritizing improvements part of the ongoing prototype process.

Problem

Managers often request training when employees are struggling with performance, even when a knowledge or skill gap may not be the real problem. The challenge was to create a short learning experience that helps managers pause and diagnose the issue before assuming more training is the answer.

Solution

I designed a one-minute, scenario-based microlearning video that introduces three simple diagnostic questions:

• Do they know how?

• Can they do it?

• What happens when they do?

The scenario follows a manager whose employees repeatedly select the wrong option in a system. As the situation unfolds, the learner discovers that employees know the correct process, but the system itself makes the correct behavior harder. The takeaway: Diagnose before you prescribe.

My Role

I led the project from concept through production, including:

• Performance problem and audience analysis

• Learning objective and instructional strategy

• Scenario and script development

• Storyboarding and visual direction

• AI-assisted visual development

• Voiceover and video production

• Final editing in Clipchamp

AI Use

I used ChatGPT as a design and development partner throughout the project. AI supported brainstorming, refinement of the learning objective, scenario development, script editing, storyboarding, visual planning, and creation of consistent corporate-style illustrations. I made the final decisions about instructional strategy, content, sequencing, visual direction, and production. The goal was to use AI to accelerate the workflow—not replace instructional design judgment.

Outcome

The final product is a concise, one-minute microlearning experience that moves from a realistic workplace problem to a practical performance-diagnosis strategy. The project demonstrates how generative AI can support faster learning development while maintaining a clear behavioral objective, scenario-based learning, purposeful visuals, and sound instructional design.

Challenge

The biggest challenge was keeping the project intentionally simple. AI makes it easy to generate more content, visuals, and production ideas than a learner actually needs. I focused on one performance problem, one decision, and one memorable framework. I also maintained visual consistency across AI-generated scenes and used limited motion and transitions so the design supported the learning instead of competing with it.

Problem

Employees have been using an informal process to submit purchase requests by emailing their managers directly. A new approval workflow is being introduced to improve routing, visibility, and consistency, but employees need to understand what has changed and what they are expected to do differently.

Solution

I designed a short, scenario-based microlearning video that focuses only on the three actions employees need to perform in the new workflow:

Choose the correct request type → Add business justification → Submit through the Approval Queue

The video contrasts the old and new processes, demonstrates each critical step, and ends with a decision point that asks the learner to apply what they learned.

My Role

I led the project from concept through production, including:

• Defining the performance goal and learning objective

• Identifying the critical workflow changes

• Developing the instructional strategy and content sequence

• Writing the script and storyboard

• Directing the visual style and diverse character representation

• Developing AI-assisted visual assets

• Producing the voiceover and final video in Clipchamp

AI Use

I used ChatGPT throughout the design and development process to help refine the scenario, script the microlearning, develop the storyboard, explore visual approaches, and generate custom illustrations. I directed and evaluated the AI output, revising visuals when character consistency, instructional clarity, or representation did not meet the design standard. AI accelerated development, while the instructional and creative decisions remained human-led.

Outcome

The result is a concise technology-adoption microlearning that turns a process change into three clear, actionable behaviors. The project demonstrates how AI-assisted development can support rapid creation of polished learning content while maintaining instructional focus, varied visual storytelling, inclusive representation, and a consistent learner experience.

Challenge

The primary challenge was deciding how much of the new workflow learners actually needed to see. A process change can quickly become a screen-by-screen software demonstration that overwhelms learners with details. I narrowed the experience to the three behaviors required for successful adoption and used visuals to show how the request moves through the workflow. I also intentionally varied characters, settings, and compositions so the learning felt inclusive and visually distinct rather than repetitive.