Artificial Intelligence in Education Management Training Course

Participants can define data controls for lawful and responsible AI-supported learning services.

• Student data lifecycle mapping from collection through retention, sharing and deletion
• Data minimisation and purpose limitation applied to learner records
• Data dictionaries defining fields, provenance, quality rules and permitted uses
• Consent, lawful basis and parental permissions for education data processing
• Role-based access controls for teachers, counsellors, administrators and vendors
• Privacy impact assessment questions for adaptive learning implementations

Participants can assess institutional readiness and evaluate AI suppliers against operational requirements.

• Readiness assessment across strategy, data, people, processes, technology and governance
• Build-buy-partner analysis using total cost, integration and exit criteria
• Vendor due diligence covering model documentation, data terms and service levels
• Interoperability requirements using LTI, OneRoster, SCORM and xAPI specifications
• Pilot selection matrix balancing value, feasibility, risk and evidence
• Business case assumptions for licensing, implementation, support and evaluation

Participants can specify AI-supported learning journeys that retain educator judgement and learner agency.

• Universal Design for Learning checkpoints embedded in adaptive learning journeys
• Competency maps translated into prerequisites, mastery thresholds and next-step recommendations
• Knowledge tracing signals interpreted without overstating learner capability
• Recommendation rules combining diagnostic results, learner goals and educator judgment
• Human-in-the-loop escalation routes for safeguarding, wellbeing and academic concerns
• Feedback prompts designed for specificity, timing, agency and metacognition

Participants can establish controlled generative AI workflows for learning content and feedback support.

• Generative AI lesson-support workflows for differentiation, feedback and content adaptation
• Prompt patterns specifying curriculum context, learner profile, constraints and output format
• Retrieval-augmented generation using approved institutional knowledge sources
• Hallucination checks using source verification, citation review and subject-matter approval
• Teacher review protocols separating draft generation from pedagogical accountability
• Age-appropriate guardrails for student-facing chatbots and virtual tutors

Participants can apply ethical and academic controls to AI-supported educational decisions and activities.

• Bias testing across language, disability, socioeconomic status and prior attainment
• Fairness measures: demographic parity, equal opportunity and error-rate comparison
• Explainability records documenting inputs, model limits, decisions and appeals
• UNESCO guidance applied to agency, inclusion, safety and human oversight
• Academic integrity controls distinguishing acceptable assistance from misconduct
• Incident response paths for harmful outputs, discrimination and data exposure

Participants can design assessment and support processes that use AI evidence appropriately.

• Assessment blueprint alignment between learning outcomes, evidence and AI-supported tasks
• Formative analytics dashboards interpreted alongside teacher observation and student voice
• Validity threats from automated scoring, proxy variables and construct underrepresentation
• Rubrics for evaluating AI-assisted work, process evidence and attribution
• Authentic assessment designs using oral defence, iteration logs and local context
• Feedback-loop metrics tracking response quality, uptake and learning progress

Participants can organise roles, approval processes and staff adoption for an AI learning initiative.

• Operating model defining product ownership, pedagogical leadership, IT and data stewardship
• RACI matrix for approvals, monitoring, incident handling and vendor management
• Change impact assessment for faculty workload, learner support and communications
• Professional development plan for teachers using AI-assisted learning tools
• Pilot protocol specifying cohorts, comparison conditions, measures and decision gates
• Procurement clauses covering audit rights, data return and model changes

Participants can evaluate whether an AI initiative improves learning outcomes fairly and credibly.

• Kirkpatrick evaluation levels adapted for AI-enabled learning interventions
• Logic model connecting inputs, activities, outputs, outcomes and institutional impact
• Baseline and comparison-group designs for credible learning-effect estimates
• Equity monitoring disaggregated by learner group, access pattern and outcome
• Dashboard metrics for adoption, engagement, mastery, retention and support demand
• Benefits realisation review comparing anticipated value with observed results

Participants can produce a prioritised implementation roadmap for their own education context.

• AI education roadmap sequencing policy, pilots, scale decisions and review cycles
• Risk register scoring privacy, bias, safety, procurement and reputational exposures
• Decision log recording evidence, approvals, exceptions and accountable owners
• 90-day implementation plan with milestones, dependencies, resources and measures
• Communications plan addressing staff questions, learner expectations and family concerns
• Personal application priorities aligned to institutional strategy and governance capacity