Course description
A focused professional learning journey.
Artificial intelligence is already influencing how learners search, write, create and make decisions. For educators, the challenge is not simply to discover new tools, but to decide when AI adds educational value, when it creates risk and how human judgement can remain visible throughout the learning process.
This practical course moves from essential AI literacy to classroom design, assessment, safeguarding and institutional planning. Participants test generative tools, examine imperfect outputs and build resources for their own context. No coding is required; the emphasis is on informed, creative and responsible educational use.
Who is this course for?
Teachers, trainers, school leaders, education staff and curriculum developers. No coding or prior AI expertise is required.
Course objectives
- Explain generative AI in clear, non-technical language
- Select AI tools according to a defined learning purpose
- Write structured prompts for planning, differentiation and feedback
- Redesign activities so learners think with AI rather than copy from it
- Address privacy, bias, copyright and academic-integrity risks
- Establish realistic boundaries for classroom and institutional AI use
Learning outcomes
By the end of the course, participants will be able to:
- Create reusable prompt frameworks for common education tasks
- Verify AI outputs for accuracy, bias and age suitability
- Produce an AI-supported lesson resource with human quality control
- Design an assessment task that makes learner thinking visible
- Draft learner guidance for transparent and responsible AI use
- Complete a classroom or school AI implementation roadmap
Detailed 5-day programme
Fifteen connected modules, from understanding to confident application.
AI Literacy Without the Hype
What Generative AI Actually Does
Build a usable mental model of large language models, training data, prediction and multimodal generation. Distinguish fluent output from verified knowledge and map familiar AI uses in everyday life.
Capabilities, Failure Modes and Human Judgement
Test the same educational task across tools, identify hallucination, inconsistency and hidden assumptions, and decide which tasks require stronger human oversight.
AI Output Investigation Lab
Compare confident, incomplete and fabricated AI responses. Trace claims, test references and create a simple routine for deciding when an output is useful, questionable or unsafe.
Practical outcome: AI opportunity-and-risk map for one teaching context
Prompting as a Professional Skill
From Vague Requests to Designed Prompts
Use role, purpose, audience, context, constraints and quality criteria to construct prompts that produce more relevant lesson ideas, explanations and differentiated materials.
Iteration, Critique and Reusable Workflows
Practise prompt chaining, example-based prompting and structured self-critique. Turn successful conversations into repeatable educator workflows rather than isolated tricks.
Prompt Design Clinic for Educators
Transform vague requests into structured prompts with role, context, learning goal, constraints and quality criteria; then improve the result through deliberate iteration.
Practical outcome: Tested prompt framework with quality checklist
Learning Design, Creativity and Inclusion
AI-Supported Lessons That Preserve Agency
Integrate AI into inquiry, project work and creative production while protecting learner voice, productive struggle and opportunities for collaboration.
Multimodal Resources and Accessible Adaptation
Create and improve text, image, audio or visual-support materials; adapt language level and format without lowering the intended learning goal.
Assessment Redesign Workshop
Rework an existing assignment so that AI use is purposeful and visible while learners still demonstrate reasoning, subject knowledge and personal contribution.
Practical outcome: Prototype AI-supported lesson sequence
Assessment, Integrity and Safeguarding
Assessment in a World of Generative AI
Analyse which tasks are easily outsourced, then redesign instructions, process evidence, feedback moments and oral reflection so authentic learning remains observable.
Privacy, Bias, Copyright and School Scenarios
Work through realistic cases involving personal data, discriminatory output, synthetic media, attribution, learner age and unequal access. Develop proportionate responses.
School AI Scenario Forum
Resolve realistic cases involving personal data, copyright, bias, unequal access and academic integrity, and translate decisions into clear classroom guidance.
Practical outcome: Redesigned assessment plus learner-use guidance
From Experiment to Sustainable Practice
Evaluating Tools and Setting Boundaries
Apply a pedagogical, accessibility, privacy and cost framework to compare tools. Define acceptable, conditional and inappropriate uses for a selected setting.
Your AI Integration Roadmap
Consolidate the week into an achievable plan with actions, responsibilities, success indicators and staff or learner support needs. Present it for structured peer challenge.
AI Integration Design Studio
Build and peer-review a classroom or institutional implementation plan with priorities, boundaries, responsibilities, evidence of impact and a realistic first pilot.
Practical outcome: Responsible AI teaching portfolio and 60-day roadmap
The sequence and examples may be fine-tuned after the pre-course needs analysis. The published learning outcomes and 30 teaching hours remain unchanged.
Teaching and learning methods
The course uses active professional learning. Short conceptual inputs are followed by investigation, creation, testing, feedback and reflection connected to each participant’s own educational context.
Your course product
A responsible AI teaching portfolio containing a prompt set, redesigned learning activity, learner guidance and a 60-day implementation plan.
European relevance
The course translates European priorities and competence frameworks into practical educational and institutional action.
Erasmus+ priorities
- Digital transformationBuilding educators’ capacity to use artificial intelligence purposefully, critically and transparently in teaching, learning and assessment.
- Inclusion and diversityUsing AI-supported adaptation to widen access while identifying bias, accessibility barriers and unequal access to technology.
- Participation in democratic lifeStrengthening critical judgement, information integrity and learner agency when automated systems influence knowledge and decisions.
- Support for education staffEquipping educators to make informed professional decisions about rapidly changing AI tools and institutional practice.
EU competence frameworks and reference instruments
- DigCompEduThe European Framework for the Digital Competence of Educators, especially Digital Resources, Assessment, Empowering Learners and Facilitating Learners’ Digital Competence.
- DigComp 3.0The European Digital Competence Framework for Citizens, including AI-related knowledge, critical information use, content creation, safety and problem solving.
- Digital Education Action Plan 2021–2027The EU policy framework for high-quality, inclusive and accessible digital education and stronger digital capacity in education systems.
- Ethical guidelines on AI and data in teaching and learningEuropean Commission guidance helping educators address human agency, fairness, transparency, privacy and responsible data use.
- EU Artificial Intelligence ActA European reference point for AI literacy, transparency and risk-aware use of artificial intelligence systems.
Assessment, validation and follow-up
Learning is evidenced through active participation, daily practical outcomes, peer and trainer feedback, the final course product and an individual transfer commitment. Participants receive a Certificate of Attendance. Learning Agreement and Europass Mobility support is available when required.