AI education, ethics and data compliance
Use AI in teaching and learning while keeping privacy, fairness and human responsibility intact, and leave with the policy, data map and review gates your organisation can adopt.
Privileged access
10 modules
6 weeks
14 to 18 hours
What you will learn
- Explain AI capabilities and limits accurately to educators, learners and leaders.
- Design learning and assessment that preserves agency, reasoning and meaningful evidence.
- Map educational data, classify risk, and evaluate an AI vendor before anyone uses it.
- Identify bias, accessibility, privacy and high-impact harms, and assign practical controls.
- Write policies, approval gates, incident response and stakeholder communications people can follow.
- Defend an allow, condition or prohibit decision to a cross-functional review panel.
Your instructors
Aaron Jimenez
View profile, Aaron Jimenez on LinkedIn
Hendrik Krack
View profile, Hendrik Krack on LinkedIn
Loc H. Nguyen, Ed.D.
View profile, Loc H. Nguyen, Ed.D. on LinkedIn
Course content
Method step 1, Choose
What these systems actually do, explained accurately enough to teach from.
- How generative AI works, in practical terms
- Test one educational task and find the failure modes
- The limitations checklist
Method step 2, Build
Keep the thinking the learner is meant to do, and let AI carry what surrounds it.
- Assistance, substitution and transformation
- Redesign one lesson with the AI-use decision tree
- The lesson redesign canvas
Method step 2, Build
Move past detection: design assessment where process is the evidence.
- Beyond detection
- Redesign one vulnerable assignment
- The disclosure form and rubric
Method step 2, Build
Know what data exists, how sensitive it is, and where it is allowed to go.
- What education data actually is
- Map the data flow for one AI-enabled use case
- The privacy impact assessment, lite
Method step 2, Build
Test with the learners who are most likely to be failed by the system.
- The harms that show up in classrooms
- Red-team an AI tutor or feedback tool
- The bias and accessibility test set
Method step 2, Build
Score the use case, then hold the vendor to what the tier demands.
- Govern, map, measure, manage
- Score three use cases and review one vendor
- The vendor questionnaire
Method step 2, Build
Turn principles into rules with owners, gates and meaningful exceptions.
- From principles to rules
- Draft the two-page policy and the quick guide
- The policy template and RACI
Method step 3, Use
Put the controls in force: review, appeal, response and the ability to stop.
- Who owns the decision
- Run the tabletop scenario
- The escalation matrix and user notice
Method step 4, Measure
Rule on real cases, defend the call, and say how you would know it was right.
- The cases that actually arrive
- Decide two scenarios and defend them to peers
- The decision rubric and peer protocol
Method step 5, Share
Assemble literacy, pedagogy, privacy, ethics and governance into one plan somebody can adopt.
- One plan a committee can approve
- Produce and present the implementation pack
- The executive summary template
Requirements
- One course, programme or team whose AI use you influence.
- Access to the policies and vendor terms that already apply to you.
- Three hours a week for six weeks.
- Plain-language writing. Every framework is introduced from the ground up.
About this course
Governance courses can stay abstract, and educator courses can leave data risk untouched. This one starts inside real teaching and learning decisions, then hands educators and leaders the policies, reviews and evidence that make adoption defensible.
Loc H. Nguyen, Ed.D. leads AI literacy, education use and responsible implementation. Qualified privacy and legal reviewers are invited for jurisdiction-specific sessions, and every legal module states plainly that requirements vary by country, province or state, institution and learner age.
This is professional education rather than legal certification. It explains the major frameworks and the obligations that commonly follow from them, including the NIST AI Risk Management Framework and its generative profile, the UNESCO competency frameworks for teachers and students, and current privacy guidance on generative AI. Legal statements are reviewed before recording and at every annual update.
Ten modules produce ten reviewable artifacts, and the capstone assembles them into one implementation pack: a use-case register, a redesigned lesson and assessment, a data map, a vendor decision, an acceptable-use policy, role quick guides, an incident playbook and an executive summary.
Certification
Complete the course. Earn your certificate.
Complete every lesson to earn a shareable certificate of completion.
Your course certificate
- Your name
- Your full name.
- The course
- AI education, ethics and data compliance
- Verification
- Verifiable online.
- Completion date
- When you finished.
Common questions
Privileged access has no course fee. Create one account to apply for Hybrid Filmmaking or join the waitlist for another course.
A course completes when your workflow runs live and you have measured it. You record a baseline in module 1, launch the workflow into a working environment, then measure the same thing again. That pairing is what makes the completion record worth sharing.
Hybrid Filmmaking accepts applications for the current intake. The other courses accept waitlist registrations for next month's intake.
Name, role, organization, and a few questions about the workflow you want to improve. It takes about a minute, and it is what lets the guided labs use your own workflow as the project.
One workflow you own, permission to change it, and the tools your team already uses. Applied AI infrastructure fundamentals also assumes a computer you can install software on, hybrid filmmaking assumes a phone or camera, and AI starter for small business runs on everyday business tools. Every course supplies sample data for the labs where your own material is restricted.
A one-page record inside the course. Before you launch, you note a baseline on time, cost, or quality. After you launch, you measure again. The sheet holds both numbers and the difference between them.
Instructor
Roan Weigert
Records all five courses
Hackathon judge and AI content creator, and host of the AI Insights San Francisco podcast. Writes this curriculum and records every core lesson.
Other courses
The other four courses
Each one takes a different kind of work through the same five steps. Pick the one closest to your job.
One permissioned campaign-to-pipeline workflow, built on your own data and measured after launch.
Combine traditional filmmaking craft with controllable AI production, then finish a hybrid film with a documented workflow.
The whole picture of an AI system, taught to the depth one real deployment needs.
Two safe AI workflows in daily use within thirty days, with the business result written down.
Join the waitlist
AI literacy for educators and learning leaders
Join now and we will hold your place for the October intake.