Course and module leaders
Map selected outcomes, labs and assessment evidence into an existing degree or continuing-education offer.
For teaching, programme, QA and professional-services teams
Add deep-tech. Keep the degree.
Eight reusable, assessment-backed mini-credentials help teaching and professional-services teams add green, digital, quantum and venture capabilities to existing courses, challenge weeks and staff-development offers. Each institution retains clear academic leadership, approval, language, timetable and recognition arrangements.
Project target: 500 academic and 500 non-academic staff successful participants, each completing at least 10 learning hours with assessment.
Existing offer+STEMMING learning package→Approved local offer
- One formal mini-credential can support several local module instances.
- Technical and pedagogical review precedes scale-up.
- Assessment, moderation, appeals and evidence retention are named.
Choose the pathway that fits your role
Four staff pathways, one common architecture.
One shared architecture supports course design, quality assurance, learning technology, student services and research-to-market teams.
Programme and QA teams
Review workload, level, issuer, evidence, moderation, appeals, certificate metadata and local recognition conditions.
Learning technology and support
Connect identity, LMS, accessible content, analytics, helpdesk and human-escalation services without centralising sensitive data unnecessarily.
TTO, careers and professional staff
Link suitable challenge evidence to internships, IP clinics, market validation and venture support under separate selection rules.
The adoption package
A practical package for confident adoption.
Each approved mini-credential should be transferable as a controlled package with enough evidence for another HEI to review and adapt it responsibly.
Competence and outcomes map
Learner-centred outcomes with relevant DigComp, GreenComp, EntreComp, EITC and EIT overarching learning-outcome references.
Teaching and lab assets
Explainers, slide or video scripts, readings, notebooks, simulations, scenarios, datasets, tutor prompts and accessibility notes.
Assessment and integrity pack
Blueprint, rubric, pass rules, allowed AI use, feedback, resubmission, identity approach, moderation, appeals and retained evidence.
AI–human support pattern
Curriculum-aware formative help, ticketing and escalation rules with qualified staff retaining high-stakes decisions.
Data and deployment notes
Privacy-preserving deployment options, data minimisation, access controls, open interfaces and local integration constraints.
Credential and reporting file
Issuer, workload, learning outcomes, assessment result, badge metadata, consent and evidence needed for project and local reporting.
From interest to a documented module instance
A six-step adoption workflow
Start with one existing offer and one mini-credential. Scale only after the first implementation has generated usable learner and staff evidence.
Select
Identify the learner need, existing course or staff-development setting and one relevant mini-credential.
Map
Compare existing outcomes, content, workload and assessment against the common mini-credential dossier.
Approve
Confirm the academic lead, quality route, teaching team, eligible cohort, language, privacy notice and recognition wording.
Adapt
Localise examples, learning links, accessibility, data access, support responsibilities and assessment logistics.
Pilot and evidence
Run a manageable cohort and retain unique-participant, learning-hour, assessment, support and feedback evidence.
Review and scale
Review learning quality, staff effort, inclusion, support demand and local constraints before the next implementation.
Common architecture
Review the eight adoption candidates
Each profile presents the shared outcome, workload and evidence model for one mini-credential. A local offer adds its confirmed teaching team, timetable, language, application route and recognition arrangements before recruitment begins.
Agentic AI · MC1Agentic AI for STEM WorkflowsDesign and evaluate a responsible AI-assisted workflow for a real STEM task.Agentic AI · MC2AI-Enabled Digital Twins and Learning AgentsBuild or evaluate an AI-enabled monitoring, digital-twin or learning-agent scenario with explicit human oversight.Smart Green & Solar · MC3Smart PV Devices, Characterisation and SystemsInterpret photovoltaic device or system evidence and make a justified engineering judgement.Smart Green & Solar · MC4Smart Solar Digitalisation and Energy Twin StudioUse a digital-twin scenario to diagnose, forecast or improve a smart-solar or energy-system condition.Quantum Trust & Security · MC5Quantum Trust and Communication FoundationsExplain quantum communication, QKD and QRNG concepts and relate them to trusted engineering systems.Quantum Trust & Security · MC6Applied Quantum Security for Critical InfrastructureDesign a proportionate transition and trusted-data architecture for a quantum-related infrastructure risk.Venture & Entrepreneurship · MC7Green Deep-Tech Opportunity, IP and Market ValidationTurn a technical idea into an evidence-based opportunity, IP and first-customer validation brief.Venture & Entrepreneurship · MC8Venture Builder, Internships and Investor ReadinessPresent a credible next-step plan for a venture, internship or research-to-market pathway.
Adoption request
Describe one concrete implementation opportunity.
You will receive a reference for the request. The relevant STEMMING team can review the proposal, clarify the next step and keep you informed through the contact details supplied.
- Name the current course or staff-development offer.
- Estimate the target cohort and preferred delivery window.
- Identify the first academic or professional contact.
Verified account required
Start with your email address.
A verified STEMMING account is required so the project can route, acknowledge and track your request securely.
Join or sign inBefore you commit
Clear answers to important questions
Each cohort publishes its own eligibility, recognition and opportunity conditions so that you can decide with confidence.
See all FAQs and supportWhat is STEMMING?
STEMMING is a European higher-education initiative developing short, assessed learning across Agentic AI, smart green and solar systems, quantum trust and security, and venture creation.
Who is the training for?
The main audiences are BSc, MSc and PhD learners, academic staff, professional university staff and selected ecosystem participants. Each cohort page confirms its own level, prerequisites and local eligibility.
How is the offer organised?
The public architecture contains four connected pillars and eight stackable mini-credentials. Local courses, challenge weeks and staff-development offers can implement those credentials in different settings.
How much time does a credential require?
A successful project completion is designed around at least 10 structured learning hours plus competence assessment. Any larger workload or local ECTS equivalence is confirmed by the delivering institution for a specific cohort.
What evidence will I produce?
Depending on the credential, evidence may include a quiz, notebook, design worksheet, scenario decision, short report, pitch or project artifact. The course page states the assessment before enrolment.