Consulting · Research · Advisory
Organizations that fail at AI adoption don't fail because the technology doesn't work. They fail because implementation ignores how people exercise judgment, navigate authority, and build trust. I study exactly that.
Start the conversationAreas of Practice
AI Adoption
Before you deploy, you need to know which roles will resist, which workflows will break, and where the real friction lives. I conduct the qualitative and organizational analysis that your IT team can't — and your vendor won't.
AI Curriculum
Most AI ethics training is too abstract to change behavior and too generic to fit your organization's context. I design curriculum grounded in communication research — built around the actual decisions your people will face.
Flagship Workshop
This workshop builds working fluency with AI tools alongside something most trainings skip: durable judgment about when to use them, when to override them, and when to decline. Rather than treating AI literacy as tool training, the workshop treats it as a human-technology relationship — participants learn to evaluate outputs critically, recognize where design choices shape behavior, and practice disclosure and ethical reasoning as active skills, not abstract discussion points. I haven't found another workshop that covers this much ground — from how computers process data through applied ethical judgment — built specifically for educators.
Workshop Goals
Course Components Menu
Topics below are modular. Sessions are built around the client's preferred focus and available time.
| Theme | Sample Components |
|---|---|
| Foundations — Hardware & Networks | How computers process and store data; the internet and how networks route information; cloud computing basics; what happens when you “send” a prompt |
| Foundations — Software (AI, ML, NLP) | AI taxonomy (narrow / general / agentic); how machine learning and NLP work; how large language models generate text; hands-on classifier training (Google Teachable Machine) |
| Prompting & Verification | Structured prompting (RTRI framework); hallucination auditing; lateral reading and fact-checking |
| Comparative Tool Evaluation | Side-by-side comparisons of text, image, and audio tools against defined evaluation criteria |
| Bias & Consequence | Bias audits across AI-generated content; privacy, intellectual property, and labor considerations |
| Interface & Interaction | Usability heuristics; dark-pattern audits; human-AI interaction principles; optional: basic game design |
| Classroom Tools & Applications | Code.org for teaching computational thinking; Google NotebookLM for source-grounded research and study support; adapting AI Assessment Scale tiers for K-12/higher-ed assignments |
| Applied Practice | Build-a-bot exercises; deepfake and synthetic-media detection; discipline-specific applications |
Not tool training. Judgment training.
Most AI training teaches you which buttons to press. Six months later the tool has changed, the buttons are somewhere else, and you're back where you started — except now your staff are using something you never approved, on client work, and nobody can explain why an output is fine or not fine beyond a gut feeling.
The tools will keep changing. What doesn't change is how these systems actually work, where they reliably fail, and what that means for the decisions you have to make.
What You'll Be Able to Decide
What Makes It Different
Tell me who's in the room and what's already going wrong, and I'll tell you what a session would look like.
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Whether you have a defined project in mind or just want to explore how this expertise applies to your context, I'm glad to talk.