What you will learn
What this course delivers
Build a safe, accountable AI governance framework
This Leader-level certification prepares principals, coordinators, HODs, IT administrators and school management — across ethics and children's rights, bias and inclusion, privacy and data protection, safety and safeguarding, content review, integrity, classroom-use policy, institutional policy, vendor due diligence, risk and incident response — with accountable human oversight at the centre of every AI decision. Educational guidance, not legal advice.
Ground every institutional AI decision in the best interests of the child, human agency, non-maleficence, accountability and proportionality — deciding yes, no or only-if against clear principles.
Protect student data across its lifecycle, recognise safety and safeguarding risks, and review AI-generated content with the VERIFY method — no identifiable student data ever enters a public AI tool.
Draft an institutional responsible-AI policy, define accountability with a RACI model, and evaluate vendors and contracts with a due-diligence scorecard — governance you can defend to the management committee.
Every module builds a governance artifact — a data map, a risk register, an incident plan, a policy, a roadmap — that assembles into a defensible School Responsible AI Governance Portfolio.
What you'll build
Every module builds a practical governance artifact, and you graduate with a defensible School Responsible AI Governance Portfolio — these sixteen sections — scored on a fourteen-criterion analytic rubric.
Educational guidance, not legal advice — have the policy reviewed by a qualified professional before adoption.
Start learningCourse Syllabus
Start here: understand how the course, activities, module assessments, final and capstone governance portfolio lead to a certificate; note the certification rules, accessibility options and privacy notice, and that the course is educational guidance not legal advice; take the baseline diagnostic and set up your governance portfolio; and use the complete course glossary as your reference throughout.
Learning Outcomes
- Describe the course structure, assessments, capstone governance portfolio and certification rules.
- Understand the course is educational guidance, not legal advice, and how the AI assistant and support work.
- Use the baseline diagnostic, governance portfolio and complete course glossary to guide your learning.
Lessons
Welcome, Navigation, Certification and Privacy
Objective: Describe how the orientation, twelve modules, assessments and capstone governance portfolio lead to a certificate, and understand the certification, accessibility, privacy and not-legal-advice terms.
Baseline Diagnostic, Portfolio Setup and Course Glossary
Objective: Take the baseline diagnostic to find your focus areas, set up your governance portfolio, and use the complete course glossary as a reference throughout.
Module Assessment
Baseline diagnostic (ungraded, 18 questions) + governance portfolio set up · 0 Questions
Visual Concepts
Timeline Visual
Course roadmap: orientation to certificate
Flowchart
Understand → Evaluate → Govern → Implement → Monitor
Checklist
Governance portfolio components
Understand what contemporary AI is and is not, how it produces outputs and where it fails, the opportunities and limitations of AI in schools, and why AI governance and human accountability are a leadership responsibility — producing an initial institutional AI-use map.
Learning Outcomes
- Explain what contemporary AI is and is not, and how it produces outputs.
- Identify the opportunities and limitations of AI in schools and common myths.
- Explain why AI governance and human accountability are a leadership responsibility, and map institutional AI use.
Lessons
Understanding Contemporary AI Systems
Objective: Explain what contemporary AI is and is not, how it produces outputs, and why that shapes what it can be trusted to do.
Opportunities and Limitations in Schools
Objective: Identify realistic opportunities for AI in schools and its limitations, distinguishing automation from augmentation.
Leadership Responsibilities and Human Oversight
Objective: Explain why AI governance and human accountability are a leadership responsibility, and produce an initial institutional AI-use map.
Module Assessment
Initial institutional AI-use map (every AI tool across teaching/admin/student-support, purpose, owner, data touched, approval status, risk note — including shadow AI) · 8 Questions
Visual Concepts
Flowchart
AI system lifecycle
Comparison Chart
Capabilities and limitations
Comparison Chart
Automation versus augmentation
Checklist
Institutional AI-use map
Ground institutional AI decisions in ethical principles and children's rights: human dignity and agency, the best interests of the child, non-maleficence, accountability, contestability, transparency and proportionality — and apply a child-centred ethical impact review before adopting AI.
Learning Outcomes
- Explain the core ethical principles for educational AI and why leaders own them.
- Apply the best interests of the child and human-agency principles to AI decisions.
- Run a child-centred ethical impact review before an institution adopts an AI tool.
Lessons
Ethical Principles for Educational AI
Objective: Explain the core ethical principles that should govern an institution's AI decisions and why leadership owns them.
Child-Centred AI Decision-Making
Objective: Apply the best interests of the child and human-agency principles to a concrete institutional AI decision using a decision tree.
Ethical Decision Laboratory
Objective: Complete a child-centred ethical impact checklist for a proposed institutional AI use, producing a documented, defensible decision.
Module Assessment
Child-centred ethical impact checklist for a proposed institutional AI use (benefit, harm, proportionality, oversight, contestability, least-intrusive alternative, decision + conditions + accountable owner) · 8 Questions
Visual Concepts
Checklist
Ethical principles for educational AI
Flowchart
Child-centred AI decision tree
Cycle Diagram
Human oversight model
Checklist
Child-centred ethical impact review
Understand how bias enters an AI lifecycle, evaluate fairness and inclusion across learner groups, and lead a bias-and-inclusion review so institutional AI does not disadvantage children by gender, language, disability, socioeconomic status or connectivity.
Learning Outcomes
- Explain how bias enters an AI lifecycle and the main sources of algorithmic bias.
- Evaluate fairness, inclusion and accessibility across diverse learner groups.
- Lead an educational AI bias-and-inclusion review before and after adoption.
Lessons
How Bias Enters an AI Lifecycle
Objective: Explain the main sources of algorithmic bias and where in the AI lifecycle each enters.
Evaluating Fairness and Inclusion
Objective: Evaluate an AI tool for fairness and inclusion across diverse learner groups, contexts and accessibility needs.
Bias-Audit Workshop
Objective: Complete an educational AI bias-and-inclusion review for a tool your institution uses or is considering, with findings and mitigations.
Module Assessment
Educational AI bias-and-inclusion review (tool, groups served, disaggregated evidence, findings, risks by group, mitigations, owner, review date, decision) · 8 Questions
Visual Concepts
Flowchart
Bias-entry pipeline
Checklist
Fairness review checklist
Comparison Chart
Unequal error rates across groups
Checklist
Bias-and-inclusion review
Map the student-data lifecycle, apply data minimisation, purpose limitation, access control and retention principles, prompt AI safely without exposing personal data, and lead a privacy impact assessment before an institution adopts an AI system.
Learning Outcomes
- Map the student-data lifecycle and apply data minimisation, purpose and retention principles.
- Prompt AI safely using anonymisation and de-identification, understanding re-identification risk.
- Lead a privacy impact assessment and build a school AI data map before adoption.
Lessons
The Student-Data Lifecycle
Objective: Map how student data flows through an institution and apply minimisation, purpose limitation, access control and retention principles at each stage.
Safe Prompting and Anonymisation
Objective: Transform a data-exposing prompt into a safe one using anonymisation and de-identification, and understand re-identification risk.
Privacy Impact Assessment Workshop
Objective: Complete a school AI data map and a privacy-impact worksheet for a proposed or existing AI system.
Module Assessment
School AI data map + privacy-impact worksheet for a proposed/existing AI system (data, purpose, lawful basis, storage, access, retention, risks, safeguards, residual risk, decision) · 8 Questions
Visual Concepts
Cycle Diagram
Student-data lifecycle
Flowchart
Data minimisation funnel
Comparison Chart
Anonymisation spectrum
Comparison Chart
Safe-prompt transformation
Recognise AI-related security and safeguarding risks — prompt injection, data leakage, deepfakes, grooming, cyberbullying and self-harm escalation — distinguish student and staff misuse, apply red-team thinking, and prepare an AI incident classification and escalation plan.
Learning Outcomes
- Recognise AI-related security and safeguarding risks in an educational setting.
- Distinguish student and staff misuse and apply red-team thinking to anticipate harm.
- Prepare an AI incident classification and escalation plan with safeguarding integration.
Lessons
AI-Related Security and Safeguarding Risks
Objective: Recognise the main AI-related security and safeguarding risks in a school and why they are a leadership concern.
Recognising Misuse and Unsafe Behaviour
Objective: Distinguish student and staff misuse of AI, apply red-team thinking to anticipate harm, and recognise the signals of an emerging incident.
Incident-Response Simulation
Objective: Prepare an AI incident classification and escalation plan that integrates safeguarding, security and privacy responses.
Module Assessment
AI incident classification and escalation plan (incident types + severity + escalation order + evidence preservation + communication + record/review, safeguarding-first) · 8 Questions
Visual Concepts
Radar Chart
AI-risk heat map
Comparison Chart
Student vs staff misuse
Flowchart
Incident recognition and escalation
Flowchart
Safeguarding escalation path
Understand why AI outputs fail — hallucination, fabricated references, outdated knowledge and overconfidence — apply the VERIFY content-review method, and require disclosure, provenance and human approval before AI-generated content is used institutionally.
Learning Outcomes
- Explain why AI outputs fail and where human review is essential.
- Apply the VERIFY content-review method to AI-generated educational content.
- Require disclosure, provenance and human approval, and know when AI output should not be used.
Lessons
Why AI Outputs Fail
Objective: Explain the characteristic ways AI outputs fail and why fluent, confident output is not evidence of accuracy.
The VERIFY Content-Review Method
Objective: Apply the six-step VERIFY method to review AI-generated educational content before it is used institutionally.
Evidence-Checking Laboratory
Objective: Complete an AI-generated content review record using VERIFY, and require disclosure and provenance for AI-assisted content.
Module Assessment
AI-generated content review record using VERIFY (checks run, findings, corrections, approver, date) + disclosure and provenance note · 8 Questions
Visual Concepts
Comparison Chart
Why AI outputs fail
Cycle Diagram
The VERIFY content-review model
Flowchart
Accuracy-verification funnel
Flowchart
Source-evaluation pyramid
Navigate copyright and licensing for AI-generated materials, uphold academic integrity in the AI era through disclosure and assessment redesign, and set an institutional AI-use and attribution protocol for teachers and students.
Learning Outcomes
- Apply copyright, licensing and attribution principles to AI-generated materials.
- Uphold academic integrity in the AI era through disclosure and assessment redesign.
- Draft an institutional AI-use and attribution protocol for teachers and students.
Lessons
Copyright and AI-Generated Materials
Objective: Apply copyright, licensing and attribution principles when an institution creates or uses AI-generated materials.
Academic Integrity in the AI Era
Objective: Uphold academic integrity when students have access to generative AI, distinguishing legitimate assistance from misconduct.
Attribution and Assessment Redesign Workshop
Objective: Draft an institutional AI-use and attribution protocol and redesign one assessment to gather authentic evidence of learning.
Module Assessment
AI-use and attribution protocol (permitted uses, disclosure, consequences) + one redesigned assessment for authentic evidence with an accessible no-AI path · 8 Questions
Visual Concepts
Flowchart
Copyright decision tree
Comparison Chart
Academic-integrity continuum
Comparison Chart
Assessment redesign model
Checklist
AI-use and attribution protocol
Set institution-wide expectations for responsible AI use in classrooms and assessment — appropriate teacher and student uses, age-based permissions, teacher review, the limits of automated grading and high-stakes decisions, student agency, and support for students without AI access — captured in an acceptable classroom AI-use matrix.
Learning Outcomes
- Set institution-wide expectations for appropriate teacher and student AI use by age and use case.
- Define the limits of automated grading, high-stakes decisions and teacher review.
- Build an acceptable classroom AI-use matrix that protects student agency and equity.
Lessons
Responsible Classroom Integration
Objective: Set institution-wide expectations for appropriate teacher and student AI use, differentiated by age and use case.
Responsible Assessment Design
Objective: Define the limits of automated grading and high-stakes AI decisions, and protect assessment security and fairness.
Classroom-Use Decision Laboratory
Objective: Develop an acceptable classroom AI-use matrix by age group and use case, protecting agency and equity.
Module Assessment
Acceptable classroom AI-use matrix (age groups × use cases, allowed/conditional/prohibited, safeguards, accessible non-AI alternative per cell) · 8 Questions
Visual Concepts
Comparison Chart
AI-use classification matrix
Flowchart
Responsible assessment model
Timeline Visual
Age-based permissions
Cycle Diagram
Student agency and teacher review
Design the components of an effective school AI policy, define governance roles and approval workflows using a RACI model and a governance committee, and draft an institutional responsible-AI policy with scope, approved and prohibited uses, oversight, complaints, review cycles and versioning.
Learning Outcomes
- Identify the components of an effective school AI policy.
- Define governance roles, approval authority and accountability using a RACI model and committee.
- Draft an institutional responsible-AI policy with scope, uses, oversight, complaints and review cycles.
Lessons
Components of an Effective School AI Policy
Objective: Identify the essential components that make a school AI policy clear, usable and enforceable.
Governance Roles and Approval Workflows
Objective: Define governance roles, approval authority and accountability using a RACI model and a governance committee.
Policy Drafting Studio
Objective: Produce a draft institutional responsible-AI policy with scope, uses, roles, oversight, complaints, review cycles and versioning.
Module Assessment
Draft institutional responsible-AI policy (scope, definitions, approved/conditional/prohibited uses, RACI roles, oversight, complaints/appeals, review cycle, versioning) · 8 Questions
Visual Concepts
Flowchart
School AI governance operating model
Comparison Chart
Governance RACI chart
Flowchart
AI policy approval flow
Checklist
Effective AI policy components
Evaluate AI tools before adoption against educational need, necessity and proportionality; assess vendor and contract risk — data ownership, model training, sub-processors, security, retention, audit rights, exit strategy and breach notification; and run a procurement simulation producing a vendor due-diligence scorecard.
Learning Outcomes
- Evaluate an AI tool against educational need, necessity and proportionality before adoption.
- Assess vendor and contract risk across data, security, continuity and exit.
- Run a procurement process and produce a documented vendor due-diligence scorecard.
Lessons
Evaluating AI Tools Before Adoption
Objective: Evaluate whether an AI tool is genuinely needed and proportionate before beginning procurement.
Vendor and Contract Risk
Objective: Assess vendor and contract risk across data ownership, model training, security, sub-processors, retention, audit rights, continuity and exit.
Procurement Simulation
Objective: Complete an AI vendor due-diligence scorecard for a real or simulated tool, producing a documented procurement recommendation.
Module Assessment
AI vendor due-diligence scorecard (need, data ownership/training, security, accessibility, bias, age, sub-processors, retention, audit, exit, breach, price; critical red-line criteria + documented recommendation) · 8 Questions
Visual Concepts
Flowchart
AI procurement stage-gate
Checklist
Vendor due-diligence scorecard
Comparison Chart
Vendor and contract risk
Flowchart
Service continuity and exit
Build and maintain an AI risk register, select controls and manage residual risk, monitor institutional AI use through indicators and audit logs, and run an incident tabletop — organised by a practical Govern–Map–Measure–Manage–Monitor–Improve governance cycle.
Learning Outcomes
- Build an AI risk register with classification, likelihood, impact, controls and residual risk.
- Monitor institutional AI use through indicators, complaints, audit logs and annual review.
- Run an incident response and continuous-improvement cycle (Govern–Map–Measure–Manage–Monitor–Improve).
Lessons
Building an AI Risk Register
Objective: Build an AI inventory and risk register that classifies each AI use by likelihood, impact, controls and residual risk.
Monitoring and Audit Controls
Objective: Monitor institutional AI use through indicators, complaints, audit logs and annual review, reporting patterns not people.
Incident Tabletop Exercise
Objective: Produce an AI risk register and incident-response workflow, and rehearse continuous improvement through a tabletop exercise.
Module Assessment
AI risk register + incident-response workflow (inventory, classification, controls, residual risk, monitoring indicators, detect→classify→contain→root-cause→act→communicate→review) · 8 Questions
Visual Concepts
Cycle Diagram
Govern–Map–Measure–Manage–Monitor–Improve cycle
Flowchart
Risk-treatment workflow
Radar Chart
Governance monitoring dashboard
Flowchart
Incident-response swimlane
Apply the whole course to realistic multi-stakeholder governance case studies, build a phased twelve-month institutional AI implementation roadmap, and assemble the School Responsible AI Governance Portfolio in the capstone studio.
Learning Outcomes
- Analyse realistic multi-stakeholder AI governance case studies and reach defensible decisions.
- Build a phased twelve-month institutional AI implementation roadmap.
- Assemble the School Responsible AI Governance Portfolio for the capstone.
Lessons
Multi-Stakeholder Governance Case Studies
Objective: Analyse realistic AI governance incidents from multiple stakeholder perspectives and reach a defensible institutional decision.
Building a Twelve-Month Implementation Roadmap
Objective: Build a phased twelve-month institutional AI governance roadmap that is realistic, prioritised and sequenced.
Capstone Portfolio Studio
Objective: Assemble the School Responsible AI Governance Portfolio from your module artifacts and complete a personal leadership commitment.
Module Assessment
Twelve-month implementation roadmap (four phases, prioritised by risk and feasibility, named owners and dates) + assembled governance portfolio components · 8 Questions
Visual Concepts
Flowchart
Multi-stakeholder case analysis
Timeline Visual
Twelve-month implementation roadmap
Checklist
Governance portfolio map
Checklist
Personal leadership commitment
AI can draft, but it does not understand or verify. You remain responsible for the accuracy, fairness, privacy and classroom-appropriateness of anything you use.