Course description
A focused professional learning journey.
AI can help educators vary language, format, scaffolding and feedback at speed, but personalisation is not automatically inclusive. Poorly designed systems can reproduce bias, expose sensitive data, narrow expectations or make learners dependent on automated support.
The course connects AI practice with Universal Design for Learning and accessibility. Participants adapt authentic materials, create multimodal supports and test differentiation workflows while keeping common goals, learner dignity and professional judgement at the centre.
Who is this course for?
Teachers, special educators, support staff and digital-learning coordinators.
Course objectives
- Connect AI-supported adaptation to UDL principles
- Identify barriers before generating personalised resources
- Create accessible text, visual and audio supports
- Differentiate scaffolds, practice and extension responsibly
- Evaluate bias, privacy, dependency and access risks
- Set clear roles for AI, educator and learner
Learning outcomes
By the end of the course, participants will be able to:
- Audit a lesson for inclusion barriers
- Build a privacy-aware learner profile for planning
- Generate and quality-check accessible multimodal resources
- Create tiered support around a shared learning goal
- Evaluate an AI tool with an inclusion rubric
- Produce an inclusive AI lesson and boundary plan
Detailed 5-day programme
Fifteen connected modules, from understanding to confident application.
AI, Learner Variability and Human Oversight
Personalisation Versus Inclusive Design
Compare reactive individual adaptation with proactive barrier reduction. Identify where AI can expand access and where it can isolate or stigmatise.
Building Safe Planning Context
Create non-identifying learner profiles that describe strengths, preferences, barriers and goals without uploading sensitive personal or diagnostic information.
AI Inclusion Opportunity-and-Risk Audit
Evaluate where AI-supported adaptation can reduce barriers and where it may expose data, stigmatise learners, narrow expectations or create dependency.
Practical outcome: Inclusion opportunity-and-risk map
Accessible Content and Multimodal Support
Plain Language Without Lost Meaning
Use AI to restructure instructions, clarify vocabulary and create examples while checking accuracy, tone, curriculum demand and cultural relevance.
Text, Visual, Audio and Alternative Formats
Plan captions, transcripts, visual schedules, image descriptions and audio support. Test whether each format truly reduces a barrier.
Accessible Multimodal Resource Lab
Create and quality-check plain-language text, visual support, captions, audio or alternative formats against a real learner need and common curriculum goal.
Practical outcome: Accessible multimodal resource set
Differentiation Around Shared Ambition
Scaffolds, Practice and Gradual Release
Generate hints, worked examples, chunked steps and guided practice that support success without completing the intellectual work for the learner.
Extension, Choice and Feedback
Design meaningful challenge, varied practice routes and feedback prompts that encourage revision and metacognition rather than passive acceptance.
Shared-Goal Differentiation Clinic
Generate scaffolds, guided practice and extension routes around one ambitious goal, then remove supports that unintentionally complete the thinking for learners.
Practical outcome: Differentiated task pathway
Equity, Bias, Privacy and Dependency
Testing for Unequal Output
Probe tools for stereotyped language, cultural assumptions, inaccessible formats and lower expectations for particular learner profiles.
Boundaries for Safe Personalised Support
Decide what data must remain offline, when automated feedback is unsuitable and how learners disclose, question and override AI assistance.
Bias and Boundary Stress Test
Probe an AI workflow for stereotypes, cultural assumptions, unequal access and inappropriate data use, and convert findings into educator and learner safeguards.
Practical outcome: AI inclusion and safeguarding rubric
Inclusive AI Lesson Studio
Integrating Resources Into Coherent Teaching
Combine common goals, accessible inputs, supported practice, learner choice and assessment evidence into one connected learning sequence.
Peer Accessibility Review and Transfer
Test the lesson through varied learner perspectives, revise weak points and plan a small classroom pilot with feedback from learners.
Inclusive AI Lesson Studio
Integrate accessible resources, differentiated support, learner choice, human feedback and explicit AI boundaries into one coherent lesson and classroom pilot plan.
Practical outcome: Inclusive AI lesson package and pilot plan
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
An inclusive AI lesson package with barrier audit, multimodal resources, differentiated supports, risk review and learner-use boundaries.
European relevance
The course translates European priorities and competence frameworks into practical educational and institutional action.
Erasmus+ priorities
- Digital transformationBuilding educators’ ability to use AI for accessible resource creation, differentiation and feedback with meaningful human oversight.
- Inclusion and diversityReducing learning barriers while guarding against biased outputs, lower expectations, stigma and digital exclusion.
- Participation in democratic lifeProtecting learner agency and the right to understand, question and challenge automated support.
- Support for education staffDeveloping professional judgement about when AI-assisted personalisation is useful, unsafe or educationally inappropriate.
EU competence frameworks and reference instruments
- DigCompEduConnecting digital-resource creation, accessibility, differentiation, feedback and learner empowerment with educators’ professional competence.
- DigComp 3.0Supporting critical, safe and responsible interaction with AI-enabled digital systems.
- Ethical guidelines on AI and data in teaching and learningGuiding decisions about fairness, human agency, transparency, privacy and responsible educational data use.
- European Strategy for the Rights of Persons with Disabilities 2021–2030Providing a rights-based context for accessibility, participation and inclusive education.
- EU Artificial Intelligence ActA reference point for AI literacy, risk awareness, accessibility and human-centred deployment of AI 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.