Business problem & context
Learning professionals need to recognize privacy, prompt-quality and factual-verification risks when using generative AI.
Interactive demonstration · fictional / 2026 · completed sample
Responsible AI for Learning Teams: interactive microlearning
A working six-minute scenario-based learning example with privacy decisions, prompt practice and human-review checks.
AI innovationLearning strategyInteractive demo
PROJECT AT A GLANCE4
Interactive learning steps
Four-step, standalone HTML learning experience01 / THE BUSINESS CHALLENGE
What needed to change.
Learning teams need to recognize privacy risks, provide appropriate context in prompts and check AI-generated claims before publication. This fictional scenario demonstrates practice instead of passive reading.
02 / MY CONTRIBUTION
Where I came in.
I designed and built the standalone interactive HTML example with branching answer feedback, an open-text prompt exercise, a two-decision knowledge check and a take-away summary.
03 / DESIGN & DELIVERY
The work behind the work.
01 ↗
Decide
Choose how to handle sensitive employee data when considering a public AI tool.
02 ↗
Practice
Rewrite a vague request using role, task, audience, source boundaries and format.
03 ↗
Verify
Identify an unsupported policy statement and choose a human-review response.
04 ↗
Reflect
Review a concise decision score and the Define → Prompt → Verify → Improve framework.
04 / VISUAL WORKFLOW
From challenge to adoption.
ILLUSTRATIVE WORKFLOW · NOT A CLIENT SYSTEM SCREENSHOTCONCEPT / Independent learning demonstration
01DecideChoose how to handle sensitive employee data when considering a public A…
→02PracticeRewrite a vague request using role, task, audience, source boundaries an…
→03VerifyIdentify an unsupported policy statement and choose a human-review respo…
→04ReflectReview a concise decision score and the Define → Prompt → Verify → Impro…
05 / OUTCOME & EVIDENCE
What can be said.
Working interactive demonstration available in the portfolio. No real learner testing, course-completion statistics, LMS integration or measured performance outcomes are claimed.
Role and attribution boundaries
Original fictional training scenario, not a client deployment and not official DoD material. No real employee data or confidential policy content is included.
HOW THE WORK CAME TOGETHER
From business need to usable experience.
An evidence-based overview. Where formal measurements or historical details are unavailable, that limitation is stated instead of filling in the gaps.
Discovery & needs analysis
Designed around common decision points: unapproved sensitive-data input, under-specified prompts and unsupported AI-generated claims.
Audience & performance gaps
Learning teams need safe, explainable choices and immediate formative feedback before relying on AI output.
Learning or technology solution
An original interactive four-step microlearning demonstration with branching feedback, prompt practice and a knowledge check.
Design & development approach
Designed a short sequence from scenario to practice to verification, using fictional content rather than client data.
Tools & implementation
Original browser-based HTML/CSS/JavaScript, responsive interaction and feedback; this is not an Articulate file or deployed client course.
Outcomes & lessons learned
Working learning demonstration, not an evaluated production course; no learner-performance, LMS or external business metrics.