AI for Aspiring Leaders

From confident personal use to leading institutional change

Alicante, Spain | 14–18 June 2027

Erasmus+ KA1 Teacher Training Course | 5 days | 25 contact hours

1. Course at a Glance

Duration: Five days, with five contact hours each day and 25 contact hours in total.

Format: Face-to-face, practical and hands-on. Each day consists of an extended morning learning block, leaving afternoons available for cultural activities, partner meetings or independent work. Participants should bring a laptop.

Group size: 12 to 18 participants, with an optimal group size of 14 to 16.

Language: English. A minimum B1 level is recommended.

Target audience: Middle leaders and aspiring leaders, including subject and department heads, year heads, programme and curriculum coordinators, digital learning coordinators, deputy principals, VET centre managers and teachers preparing for leadership roles.

Entry requirements: No technical prerequisites apply. Participants should bring one recurring leadership task or institutional challenge that they would like to address during the course.

Certification: Participants receive a Certificate of Attendance for 25 contact hours, support with Europass Mobility documentation and a learning agreement mapped to DigCompEdu and DigCompOrg.

Framework alignment: DigCompEdu, DigCompOrg, SELFIE, the EU Digital Education Action Plan 2021–2027, EU ethical guidance on using AI and data in education, GDPR and the EU AI Act.

2. Course Rationale

Educational leaders are increasingly expected to make decisions about artificial intelligence, including which tools to adopt, how they should be funded, what policies are required and how learner and staff information can be protected.

This course is designed specifically for those who lead departments, programmes, centres and educational institutions. It moves through three connected stages:

  • Personal competence: Developing the confidence to use AI tools effectively and understand their limitations.
  • Educational judgement: Evaluating the implications of AI for teaching, learning, assessment, academic integrity and digital literacy.
  • Institutional leadership: Using AI responsibly within leadership practice and developing appropriate approaches to policy, procurement, staff development and organisational change.

The course concludes with the development of a practical AI adoption roadmap that participants can bring back to their institution.

3. Learning Outcomes

By the end of the course, participants will be able to:

  • Explain in accessible language how generative AI and large language models produce content.
  • Recognise hallucinations, bias and other limitations in AI-generated material.
  • Apply structured prompting techniques to produce professional outputs.
  • Establish effective habits for checking and verifying AI-generated information.
  • Distinguish evidence-based educational practice from unsupported marketing claims.
  • Evaluate AI-supported lessons, resources and assessments using sound pedagogical criteria.
  • Understand the limitations of AI detection software and respond fairly to academic integrity concerns.
  • Develop assessments and activities that strengthen learners’ AI and digital literacy.
  • Identify institutional responsibilities under GDPR and the EU AI Act.
  • Examine the data protection and safeguarding practices of technology providers.
  • Use AI to support leadership tasks while retaining responsibility for professional judgement.
  • Develop a realistic institutional AI adoption roadmap.
  • Respond constructively to staff concerns or resistance.
  • Identify areas of leadership practice that should not be delegated to AI.

4. Course Methodology

The course is based on practical leadership challenges rather than demonstrations of technology.

Approximately 70 per cent of the course is devoted to hands-on activities. Participants work with authentic leadership tasks and institutional challenges drawn from their own professional settings.

The methodology includes:

  • Practical workshops and guided activities.
  • Build-and-critique exercises.
  • Individual and group reflection.
  • Cross-national and cross-sector collaboration.
  • Critical evaluation of AI-generated content.
  • Application of learning to participants’ institutions.
  • Daily reflection leading to the final adoption roadmap.

The course takes a deliberately critical approach. Participants learn not only how AI may support their work, but also when its output is unreliable, inappropriate or unsafe.

5. Pre-Course Preparation

Before attending, participants will be asked to:

  • Identify a recurring leadership task that consumes a disproportionate amount of time.
  • Bring an institutional challenge that AI might or might not help to address.
  • Set up accounts for the platforms used during the course.
  • Complete or review their institution’s SELFIE self-reflection report, where available.
  • Bring any relevant organisational or national guidance on AI, digital technologies and data protection.
  • Complete a short needs-analysis form outlining their role, responsibilities, institutional setting and learning priorities.

Participants must not bring confidential learner, staff, safeguarding or HR information for use in any AI platform. [AI-for-Asp…e-Proposal | PDF]

6. Programme Outline

Day 1: How AI Works and How to Use It Safely

Participants develop an accessible understanding of generative AI, explore a selection of AI and digital platforms, practise structured prompting and examine hallucination, bias, attribution, confidentiality and verification.

The day also addresses GDPR, learner data, the EU AI Act and responsible technology procurement.

Day 2: Educational Practice in the AI and Digital Era

Participants examine how AI is influencing personalisation, differentiation, formative assessment and lesson design.

They redesign an educational activity and critically evaluate whether the use of AI adds genuine educational value.

Day 3: Assessment, Integrity and Student Digital Literacy

This day focuses on academic integrity, assessment design and the limitations of AI detection software.

Participants explore fair approaches to investigating concerns, consider how AI literacy can be taught and begin drafting appropriate institutional guidance.

Day 4: AI Tools for Educational Leaders

Participants examine the use of AI in scheduling, reporting, communications, documentation and decision support.

The day also considers confidentiality, delegation, institutional metrics and the limits of automated decision-making.

Day 5: Leading Organisational Change

The final day addresses change management, staff engagement, resistance, budgeting, procurement, vendor dependence and organisational capacity.

Participants complete and present a practical AI adoption roadmap for their institution. [AI-for-Asp…e-Proposal | PDF]

7. Capstone Project

Each participant, or pair of participants, will develop a 90-day AI Adoption Roadmap for their institution.

The roadmap will include:

  1. A clearly defined institutional problem or workflow.
  2. A proposed approach and evaluation of the selected technology.
  3. Consideration of alternative approaches.
  4. A data protection and risk assessment.
  5. A staff development plan.
  6. Realistic indicators of success.
  7. A clear condition under which the project should be stopped or reconsidered.
  8. A one-page briefing for a principal, senior leadership team, board or governing body.

Participants will present their roadmap and receive structured peer and facilitator feedback. [AI-for-Asp…e-Proposal | PDF]

8. Tools and Resources

The course may include practical engagement with:

  • General-purpose AI assistants for drafting, analysis and communication.
  • NotebookLM for source-grounded research and document analysis.
  • Canva for professional visual communication.
  • Gamma for presentations and briefing materials.
  • DigCompEdu and DigCompOrg.
  • SELFIE.
  • The EU Digital Education Action Plan.
  • EU ethical guidance on AI and data in teaching and learning.
  • The EU AI Act.
  • UNESCO AI competency frameworks.
  • European School Education Platform and eTwinning resources.

9. Assessment and Certification

Assessment is practical and developmental.

Formative assessment includes:

  • Daily reflection activities.
  • Peer review.
  • Facilitator feedback.
  • Policy-drafting exercises.
  • Practical build sessions.
  • Change-management activities.

Summative assessment includes:

  • Completion of the institutional AI adoption roadmap.
  • A short presentation of the proposed roadmap.

Participants receive:

  • A Certificate of Attendance detailing 25 contact hours.
  • A completed learning agreement mapped to DigCompEdu and DigCompOrg.
  • Support with Europass Mobility documentation, where required by the sending organisation.

10. Follow-Up and Impact

Participants will receive access to a shared collection of course resources and completed adoption roadmaps.

An optional online follow-up session may be offered to examine what participants implemented, what challenges arose and which approaches were amended or discontinued.

Participants will also be encouraged to share their learning through:

  • Staff development activities.
  • Senior leadership or board presentations.
  • Professional and educational networks.
  • eTwinning partnerships.
  • The European School Education Platform.