Vibe Coding Academy

    High school skills mapping · Vibe Coding Academy

    High school skills mapping: every week, mapped to recognized frameworks.

    Each of the 16 weeks is aligned with UNESCO, DigComp 3.0, ISTE and OECD — linking observable student skills to competency codes, student-produced evidence and a verification method. High schools can assess what students can demonstrably do, not just attendance or completion.

    Frameworks used

    Four international reference points.

    UNESCO

    UNESCO AI Competency Framework for Students

    Twelve competency blocks across human-centred thinking, AI ethics, AI techniques and applications, and AI system design — at Understand, Apply and Create levels.

    Official source
    DigComp 3.0

    European Commission DigComp 3.0

    The EU reference framework of 21 digital competences, with AI knowledge, skills and attitudes integrated throughout.

    Official source
    ISTE

    ISTE Standards for Students

    Seven standards — from Empowered Learner and Digital Citizen to Innovative Designer and Global Collaborator — with individual indicators.

    Official source
    OECD

    OECD Learning Compass 2030

    The transformative competencies of creating new value, reconciling tensions and dilemmas, and taking responsibility — plus student agency.

    Official source

    UNESCO competency codes used on this page

    H1Human agency
    H2Human accountability
    H3Citizenship in the era of AI
    E1Embodied ethics
    E2Safe and responsible use
    E3Ethics by design
    T1AI foundations
    T2Application skills
    T3Creating AI tools
    D1Problem scoping
    D2Architecture design
    D3Iteration and feedback loops

    Week-by-week mapping

    Skills, evidence and verification for all 16 weeks.

    Program-level outcomes

    What successful students can demonstrate.

    By the end of the 16-week program, successful students should be able to demonstrate competency across seven areas.

    AI literacy

    • Explain how AI-assisted tools contribute to product development.
    • Evaluate AI output instead of accepting it automatically.
    • Use AI purposefully and responsibly.
    • Document the role of AI in their work.
    • Recognize common limitations, including inaccurate or fabricated output.

    Digital product development

    • Scope a problem.
    • Define user needs.
    • Design a solution.
    • Build and deploy a functional prototype.
    • Test and refine the prototype.
    • Work with data, authentication, agents and automations at an age-appropriate level.

    Information and data literacy

    • Conduct targeted research.
    • Evaluate source credibility.
    • Organize findings.
    • Interpret user and product data.
    • Use evidence to make decisions.

    Digital citizenship and ethics

    • Protect personal information.
    • Respect copyright and licenses.
    • Disclose synthetic content appropriately.
    • Consider accessibility and inclusion.
    • Maintain human responsibility for consequential decisions.

    Entrepreneurship

    • Identify problems worth solving.
    • Validate customer needs.
    • Create a value proposition.
    • Evaluate pricing and demand.
    • Develop a go-to-market plan.
    • Communicate product value.

    Communication and collaboration

    • Conduct interviews.
    • Give and receive feedback.
    • Adapt messages for different audiences.
    • Present complex ideas visually.
    • Demonstrate and defend a completed product.

    Student agency

    • Set goals.
    • Select appropriate tools.
    • Manage an extended project.
    • Respond constructively to uncertainty and failure.
    • Reflect on progress.
    • Identify future learning needs.

    Assessment model

    Completion is not verification.

    A student does not receive credit for a skill just because the lesson was completed. A skill is verified only when the student:

    Explore the curriculum
    1. 01Produces an observable artifact.
    2. 02Demonstrates the artifact in practice.
    3. 03Explains the reasoning behind key decisions.
    4. 04Identifies limitations or risks.
    5. 05Responds to feedback.
    6. 06Submits a revised or improved version where required.

    References

    Competency codes on this page (UNESCO H1–D3, DigComp 1.1–5.4, ISTE 1.1–1.7 and indicators such as 1.4.c) follow the numbering in the sources below.

    1. [1]UNESCO (2024). AI competency framework for students. Paris: UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391105
    2. [2]Cosgrove, J. & Cachia, R. (2025). DigComp 3.0: European Digital Competence Framework, 5th ed. JRC144121. Luxembourg: Publications Office of the European Union. https://joint-research-centre.ec.europa.eu/projects-and-activities/education-and-training/digital-transformation-education/digital-competence-framework-digcomp/digcomp-30_en
    3. [3]ISTE (2016). ISTE Standards for Students. International Society for Technology in Education. https://iste.org/standards/students
    4. [4]OECD (2019). OECD Future of Education and Skills 2030: OECD Learning Compass 2030. Paris: OECD. https://www.oecd.org/en/data/tools/oecd-learning-compass-2030.html

    Framework names and codes belong to their respective organizations. This mapping is the academy's own alignment and does not imply endorsement by UNESCO, the European Commission, ISTE or the OECD.