Generative AI in Schools and Universities Course: Learning Design, Integrity and Policy

Programme Summary

Generative AI in schools and universities is the subject of this one-week training course for education system and institutional leaders, ending in an AI adoption plan for one institution. Students and staff already use generative tools, often before any rule exists on what is allowed. The programme moves from learning use cases to assessment and integrity, teacher capacity, data protection and policy, using cases from primary schools, secondary schools and universities. Mars Training Centre UK delivers this programme on generative AI in schools and universities to mixed-organisation cohorts.

The Business Case

Generative tools change how learners write, research and revise, and how staff plan and mark. Without shared rules, institutions face uneven practice, contested grades and exposure of student data to suppliers. A common method lets leaders decide where AI supports learning, where it must be restricted and how the institution will know the difference.

• Assessment Credibility: Improves as tasks and marking rules are redesigned for an AI-enabled setting.
• Teaching Quality: Improves as staff capacity and agreed classroom uses replace unguided experiment.
• Data Protection Exposure: Falls as tools are validated before adoption and student data is mapped.

Principles and Evidence Base

• UNESCO Guidance for Generative AI in Education and Research: Sets out a human-centred approach, data privacy protection, teacher capacity building and tool validation, applied in Sessions 1, 3, 4 and 5.
• Constructive Alignment: Links learning outcomes, teaching activity and assessment, applied in Sessions 1 and 2.
• Academic Integrity Principles: Distinguish permitted support from misrepresented work, applied in Session 2.

Intended Participants

• School system leaders who set direction for teaching and technology across many schools.
• Deans and provosts who own academic standards and institutional policy.
• Heads of teaching and learning who guide staff practice and development.
• Curriculum and assessment leads who redesign tasks and marking rules.
• Education technology directors who select, validate and secure digital tools.

Typical settings include school networks, universities, vocational colleges and education ministries.

Learning Outcomes

1. Participants will be able to prioritise learning use cases for generative AI by educational value and risk.
2. Participants will be able to redesign assessment tasks so that they remain valid when learners use generative tools.
3. Participants will be able to construct an integrity procedure that separates permitted support from misrepresented work.
4. Participants will be able to evaluate a staff capacity plan against the practices expected in classrooms.
5. Participants will be able to diagnose data protection and equity risks in a tool before adoption.
6. Participants will be able to justify an institutional AI policy and adoption plan for one institution.

Programme Structure

Day 1: Generative AI in Schools and Universities and Learning Design

Where does generative AI serve learning, and where does it weaken it?

• Learning Use Case Inventory
• Constructive Alignment Map
• Educational Value and Risk Matrix
• Age and Level Suitability Grid
• Human-Centred Use Principles

Day 2: Assessment and Academic Integrity

How can assessment stay valid when learners use generative tools?

• Assessment Task Redesign Template
• Permitted Support Statement
• Integrity Case Procedure
• Marking Rubric Adjustment
• Oral and Supervised Assessment Options

Day 3: Teacher Practice and Capacity

What must staff be able to do, and how is that built?

• Staff Capability Framework
• Classroom Practice Examples
• Prompt Use in Lesson Planning
• Workload Impact Review
• Development Programme Outline

Day 4: Data Protection, Equity and Tool Validation

Is a tool safe, fair and suitable for the learners who will use it?

• Student Data Flow Map
• Tool Validation Checklist
• Supplier Terms Review
• Access and Equity Review
• Bias and Language Fit Check

Day 5: AI Adoption Plan for One Institution

Do the earlier records add up to one workable plan?

• Applying the Session 1 Learning Use Case Inventory
• Testing the Session 2 Permitted Support Statement
• Reviewing the Session 3 Staff Capability Framework
• Checking the Session 4 Tool Validation Checklist
• Completing the Institutional AI Adoption Plan

Methodology

The programme uses a structured exercise method built on a shared case of an institution where students, staff and suppliers already use generative tools without common rules. Participants work on prepared assessment tasks, policies and tool terms and then adapt each artefact.

• Suggested activity: Redesigning one written assessment so that it still measures the intended learning outcome.
• Suggested activity: Reviewing a tool supplier's terms against a student data flow map.
• Suggested activity: Presenting a draft policy to a mock academic board.

Capstone Output

Each participant completes an AI adoption plan for one institution. It holds a prioritised list of learning uses, a redesigned assessment, an integrity procedure, a staff capability plan, a tool validation record and a draft policy. The plan is used at work to brief governing bodies, brief staff and set the conditions under which tools may enter classrooms.

Frequently Asked Questions

Is generative AI in schools and universities training suitable for leaders without a technology background?

Yes. Generative AI in schools and universities is taught through institutional cases and templates. It assumes experience in teaching, curriculum, assessment or education management but no programming skills.

Which guidance does generative AI in schools and universities training rest on?

Generative AI in schools and universities training rests on the UNESCO guidance for generative AI in education and research, constructive alignment and academic integrity principles, applied to policy, assessment and tool choice.

How is the plan from generative AI in schools and universities training used at work?

You use the adoption plan to brief governing bodies and staff, set rules for classroom and assessment use and decide which tools may be validated for adoption.

How does generative AI in schools and universities differ from general educational technology training?

Generative AI in schools and universities centres on content-generating tools, assessment validity and integrity, while general educational technology training covers learning platforms, devices and digital infrastructure.