I’ve designed and produced video-based learning across dozens of SaaS products (e.g., Salesforce, AWS, ChatGPT, Adobe, MailChimp and Microsoft 365).
Example 1: LinkedIn Hiring Assistant in Recruiter
🎬 My Role
Supervising Producer/Instructional Designer: I led the end-to-end production from course flow to recording and post-production. Collaborated closely with LinkedIn’s product trainer to ensure the training reflected both the product’s capabilities and recruiters’ real-world needs.
📘✨Course Overview
Title: LinkedIn Hiring Assistant in Recruiter
SaaS products: LinkedIn Hiring Assistant
Learner level: Beginner
Learning objectives: See below.
Duration: ~30 minutes
🧠 Learning Goals
The course helps the user:
Explain what the LinkedIn Hiring Assistant does and how it fits into the recruiter workflow
Set up hiring projects and tailor job requirements using AI assistance
Interpret AI-generated insights to make better hiring decisions
Balance automation with a human-centered approach to recruiting
🔗 Explore the Course on LinkedIn Learning or watch sample videos below:
⚙️ Production Highlights
Partnered with product SMEs and the LinkedIn Talent team to define key learning outcomes
Guided instructors through script development, pacing, and tone for on-camera clarity
Coached instructor to capture a more engaging, warm narrative tone
Chose a story-based approach from the beginning
Delivered a course optimized for engagement, accessibility, and visual storytelling
📈 Impact
The course helps talent professionals adopt AI responsibly and strategically—reducing manual work while improving candidate experience. It also serves as one of LinkedIn’s first educational resources showcasing how AI is transforming recruiting to serve LinkedIn Learning customers.
4.7 / 5 (94%) customer feedback rating.
Here you go — same content, just with emojis added to enhance readability and structure. Nothing else changed.
ID one pager_GPT-4 Foundations: Building AI-Powered Apps
🎬 My Role
Supervising Producer/Instructional Designer: I led the end-to-end production from course flow to recording and post-production. Collaborated closely with LinkedIn’s product trainer to ensure the training reflected both the product’s capabilities and recruiters’ real-world needs
📘✨Course Overview
Title: GPT-4 Foundations: Building AI-Powered Apps
SaaS products: ChatGPT, Voiceflow
Learner level: Beginner / Introductory to AI apps using large language models (LLMs)
Learning objectives here.
Duration: ~1 hour 3 minutes
Synopsis: The course introduces learners to the basics of large language models (LLMs) to build no-code apps.
🔗 Explore the Course on LinkedIn Learning .
📈🎯Business Strategy
Using the OpenAI Playground and GPT-4, this course taps into cutting-edge LLMs and generative AI, which is highly motivating for learners.
The relevance of the content increases engagement: learners feel they’re acquiring real, actionable skills in a trending domain.
The “why this matters / how to start” framing at beginning intentionally helps learners value the course and stay motivated.
With a duration of just over an hour, the course is short enough for busy professionals, yet long enough to cover meaningful content
Micro-learning style supports the user’s busy schedule (you, as an experienced supervising producer, appreciate bite-sized learning)
This increases completion rates and makes it easier to integrate into workflow or professional development.
Allows more flexibility when updating content.
🧠🛠️ Instructional Design Principles
Here’s a breakdown of the key instructional design elements that contribute to its effectiveness:
Clear scaffolding from foundational to applied
Project-based: goal is to create a non-code chatbot to meet terminal objective.
Bloom’s taxonomy progression: learners move from understanding (“what”) to doing (“how”) to creating (“build an app”) — a classic “understand → apply → create” progression aligned with Bloom’s taxonomy.
This progression supports cognitive load management: early segments build conceptual grounding; later segments apply those concepts in real-world tasks.
Active learning / challenge tasks
The course includes “challenge” sections at the end of each chapter/module (e.g., “Challenge: Generating a superpower for Bubbles”; “Challenge: Placing a Binaryville character order”).
These tasks give learners the chance to practice and reflect, rather than passively watch. This supports retrieval, generative practice and deeper encoding.
Having “solutions” also provides immediate feedback/reference, which is helpful for self-paced learners.
Real-world, applied project (no-code chatbot)
This project-based element enables transfer of learning — from prompt engineering concepts to building a tool/application.
The final module: “Building a BigStarCollectables Chatbot” brings it all together. It challenges learners how to build a no-code chatbot, customize it, and apply prompt chaining.
For learners who may not be strong coders (or prefer no-code/low-code), this is inclusive and provides a tangible deliverable.
Layered prompting complexity
The course deliberately escalates the complexity of prompting: from simple prompts to system-level prompts, adding context, persona, chaining. linkedin.com+1
This teaching of “how to ask the model/LLM the right way” is crucial for generative AI literacy.
Also aligns with ‘scaffolding’ — start simple, add complexity, support learners as they build capability.
Accessible and inclusive for non-experts
The course is labelled as “Beginner” and gives a visual, simple introduction to building AI-powered applications. Kalamazoo Public Library+1
Using no-code tools lowers the barrier to entry. This helps widen the audience and supports a growth mindset (“I can learn this even if I’m not a programmer”).
By providing a clear path, the learner is more likely to succeed and less likely to feel lost.
Solution-oriented and outcome-driven
The end goal isn’t just “understand prompting” but “build a chatbot” — a deliverable that demonstrates capability.
This aligns with performance-based learning: the learner shows what they can do, not just what they know.
Having a tangible output (chatbot) reinforces motivation and retention.
Link to broader skill ecosystem
The course is positioned within larger learning paths (“Hands-On Projects for OpenAI-Powered Apps”, “Develop Your Skills with Large Language Models”).
This means learners see where this course fits in a progression — helpful for planning ongoing growth and career relevance.
Instructional Strategies
Scaffolding: Gradual build from simple to complex.
Active Learning: Learners engage in challenge tasks after each module.
Project-Based: The final build of a chatbot gives a tangible artifact.
Low Barrier to Entry: No heavy prerequisites; uses no-code tools, accessible visuals.
Motivational Framing: Relevance to trending domain (generative AI) keeps learners engaged.
Micro-learning: Short duration modules fit busy schedules.
Solution + Feedback: Providing solutions to challenges supports self-paced learning and the “check my work” mindset.
Transfer Focus: Built such that learners can apply skills in real contexts (e.g., business bot, productivity assistant etc.).
Progression Pathway: Situated within a broader learning ecosystem so learners know “what’s next.”
📝⚙️ Assessment & Practice
Formative challenge tasks after each module (immediate low-stakes practice).
Summative project: Build and customize a chatbot application—demonstration of learning.
📊🏆Metrics for Success
Increase in prompt engineering usage in job/work context (post-course sentiment).
4.7 / 5 (90k+ views)