Vibe Coding Academy

    University skills mapping · Vibe Coding Academy

    University learning outcomes, mapped to recognized frameworks.

    The 16-week applied AI, digital product development and entrepreneurship curriculum, aligned with UNESCO, DigComp 3.0, EntreComp and NACE — so universities can evaluate what students can demonstrably know, apply, create and defend.

    Intended academic use

    Flexible formats for your institution.

    The university retains authority over academic credit, entry requirements, assessment policies and program approval.

    Frameworks used

    Four international reference points.

    UNESCO

    UNESCO AI Competency Framework

    AI competence across human agency, ethics, AI techniques and system design — a useful reference for introductory university programs, contextualized to each discipline.

    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
    EntreComp

    EntreComp: Entrepreneurship Competence Framework

    15 competences across ideas and opportunities, resources, and into action — acting on ideas to create financial, social or cultural value.

    Official source
    NACE

    NACE Career Readiness Competencies

    Eight competencies employers look for, demonstrable across university, internship and employment contexts.

    Official source

    Course-level learning outcomes

    Upon successful completion, students should be able to:

    CLO1

    Opportunity identification

    Identify and evaluate a meaningful market, organizational or social problem using credible digital research and stakeholder evidence.

    CLO2

    Responsible AI use

    Apply generative AI critically, transparently and responsibly while maintaining human accountability for decisions and outputs.

    CLO3

    Product definition

    Translate stakeholder needs into a structured value proposition, product brief, user flow and minimum viable product specification.

    CLO4

    Digital product development

    Design, build and deploy a functional digital product using AI-assisted and no-code or low-code development tools.

    CLO5

    Data, privacy and security

    Apply appropriate data-management, authentication, privacy, access-control and security principles to a prototype.

    CLO6

    AI agents and automation

    Design, build and evaluate bounded AI agents and automated workflows with defined human-oversight mechanisms.

    CLO7

    Evidence-based iteration

    Collect, analyze and apply user feedback, analytics and test results to improve a product.

    CLO8

    Commercial and entrepreneurial reasoning

    Evaluate customer value, pricing, cost assumptions, market channels, risk and basic unit economics.

    CLO9

    Professional communication

    Communicate product, technical and commercial decisions effectively to academic, customer and industry audiences.

    CLO10

    Reflective professional development

    Evaluate personal capability, respond to feedback and identify future learning and career-development needs.

    Week-by-week mapping

    Outcomes, evidence and verification for all 16 weeks.

    Explore the curriculum

    Recommended assessment structure

    How the course can be graded.

    Opportunity research and problem definition10%
    Customer and stakeholder discovery10%
    Offer and validation experiment10%
    Functional minimum viable product20%
    Data, privacy, security and responsible AI10%
    AI agent or workflow automation15%
    Go-to-market and financial reasoning10%
    Final portfolio, demonstration and defense15%
    Total100%

    Responsible AI, privacy and academic integrity should operate as threshold requirements. A student should not pass solely on the strength of a technically impressive product if the work demonstrates unsafe, deceptive or unethical practice.

    Performance rubric

    Four university-level performance levels.

    Standard of evidence

    1 — Introductory

    Recognizes concepts but cannot apply them reliably.

    Incomplete evidence, excessive dependence on supplied templates or inability to explain decisions.

    2 — Developing

    Applies concepts in a familiar context with guidance.

    Partially functional work with limited testing or justification.

    3 — Proficient

    Applies skills independently and justifies decisions with evidence.

    Functional output, documented process, appropriate testing and defensible reasoning.

    4 — Advanced

    Integrates, transfers and critically evaluates skills in a complex context.

    Universities may convert these levels into their existing grade, GPA or mastery system.

    Academic integrity and AI verification

    Verifying work in an AI-assisted course.

    Because the course permits and teaches AI-assisted production, traditional plagiarism detection is insufficient. Student attainment should be verified through:

    • Prompt and AI-interaction logs.
    • Version history.
    • Product changelogs.
    • Research notes.
    • Design decisions.
    • Test records.
    • Individual reflective commentary.
    • Live product demonstrations.
    • Unseen change requests.
    • Oral questioning or viva.
    • Individual contribution statements.
    • Peer assessment for team projects.
    • Clear citation of third-party components.
    • Disclosure of generated media and code.
    • Faculty comparison of process evidence with final output.

    A student who cannot explain, test or modify submitted work should not be considered to have demonstrated mastery.

    Minimum completion requirements

    A student should be required to:

    • Submit all major assessment components.
    • Produce a functional prototype.
    • Complete the responsible-AI and privacy requirements.
    • Demonstrate the product live.
    • Defend key technical and commercial decisions.
    • Document their individual contribution.
    • Complete a final professional reflection.
    • Achieve the university’s approved minimum grade.

    A recommended course pass threshold is 60%, but the institution should apply its own academic regulations.

    Accreditation alignment

    Supporting AACSB and ABET evidence.

    AACSB business education standards

    For business schools, the curriculum may contribute evidence toward AACSB curriculum goals, particularly the current Standard 4 provisions addressing curriculum currency, innovation, technology and workforce readiness.

    AACSB Standard 4.1

    The program uses:

    • Current and emerging AI technologies.
    • Contemporary digital product practices.
    • Market and customer evidence.
    • Applied entrepreneurship.
    • Technology-enabled business-model development.
    • Experiential and project-based learning.

    AACSB Standard 4.2

    The course supports systematic assessment through:

    • Published course learning outcomes.
    • Direct assessment artifacts.
    • Rubrics.
    • Student-level performance data.
    • Pre- and post-course competency reviews.
    • Cohort-level outcomes reporting.
    • Documented curriculum-improvement recommendations.

    AACSB Standard 4.3

    The course develops:

    • Digital and technological agility.
    • Innovation and adaptability.
    • Critical evaluation of technology-generated outputs.
    • Responsible application of technology.
    • Business problem-solving.
    • Workforce readiness in a technology-forward environment.

    This mapping does not mean that the course or provider is AACSB accredited. An AACSB-accredited school must determine how the course contributes to its own mission, competency goals, assurance-of-learning system and accreditation evidence.

    Optional ABET engineering outcomes

    When contextualized around an appropriate engineering problem, the curriculum may support elements of the following ABET student outcomes.

    Outcome 1: Identify, formulate and solve complex engineering problemsProblem scoping, requirements definition, system decomposition and prototype development
    Outcome 2: Apply engineering design within relevant constraintsArchitecture design, accessibility, safety, privacy, economics and iterative product development
    Outcome 3: Communicate effectively with different audiencesCustomer interviews, technical documentation, demonstrations and final presentations
    Outcome 4: Recognize ethical and professional responsibilitiesResponsible AI, privacy, security, synthetic media and human accountability
    Outcome 5: Function effectively on a teamShared project responsibilities, collaboration, peer feedback and project management
    Outcome 6: Conduct experimentation and analyze dataUser tests, beta experiments, product analytics and evidence-based iteration
    Outcome 7: Acquire and apply new knowledgeIndependent tool learning, responding to feedback and adapting to emerging technologies

    For ABET use, engineering faculty must ensure that:

    • The selected project qualifies as an engineering problem.
    • Appropriate mathematics, science or engineering principles are applied.
    • Discipline-specific standards and constraints are included.
    • Assessment is integrated into the program’s approved continuous-improvement process.

    The general Vibe Coding curriculum alone should not be represented as satisfying an ABET outcome in full.

    Recommended evidence portfolio

    Each student maintains a portfolio containing:

    Competency baseline.
    AI-use protocol.
    Research and source analysis.
    Opportunity assessment.
    Customer-discovery documentation.
    Product-requirements document.
    Value proposition.
    Validation landing page.
    User flows and architecture.
    Prompt and decision logs.
    MVP versions.
    Data and privacy documentation.
    Security tests.
    Design and accessibility review.
    Beta feedback.
    Analytics.
    Agent or automation documentation.
    Evaluation datasets and results.
    Content portfolio.
    Go-to-market strategy.
    Pricing and financial model.
    Sales or stakeholder communication.
    Launch evidence.
    Final product.
    Final presentation.
    Individual contribution statement.
    Professional-development reflection.

    The portfolio should demonstrate the student’s process, not only the final output.

    References

    Competency codes on this page (UNESCO U-H1–U-D3, DigComp D1.1–D5.4, EntreComp ENT1.1–ENT3.5, NACE N-CD–N-TECH) are the academy's shorthand for the competences in the sources below. NACE currently lists its Equity and Inclusion competency as under review; universities may substitute their own inclusion outcomes.

    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]Bacigalupo, M., Kampylis, P., Punie, Y. & Van den Brande, G. (2016). EntreComp: The Entrepreneurship Competence Framework. JRC101581. Luxembourg: Publications Office of the European Union. https://publications.jrc.ec.europa.eu/repository/handle/JRC101581
    4. [4]National Association of Colleges and Employers (NACE). Career Readiness: Competencies for a Career-Ready Workforce. https://www.naceweb.org/career-readiness/competencies/career-readiness-defined/

    This page describes curriculum alignment. It does not imply accreditation, certification or endorsement by UNESCO, the European Commission, NACE, AACSB or ABET.