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DCI AI Hub — AI Tracker socialprotectionai.org/use-case/EGY-001
EGY-001 Exported 3 April 2026

ILO PROSPECTS Egypt – Skills Profiling Application (SkillLab)

Country Egypt, Arab Rep.
Deployment Status Pilot / Controlled Trial Phase
Confidence Confirmed
Implementing Agency International Labour Organization (ILO PROSPECTS Egypt); Association of Businessmen in Alexandria (ABA) (pilot delivery); Caritas (JSC delivery partner)

Overview

The ILO PROSPECTS Egypt Skills Profiling Application is an AI-driven career guidance tool developed by SkillLab, a social enterprise based in Amsterdam, the Netherlands, and deployed in Egypt through a partnership between the International Labour Organization (ILO) PROSPECTS programme, SkillLab, and the Association of Businessmen in Alexandria (ABA). The application was piloted between 2020 and 2021 to assist refugees and host community members in Egypt in identifying, articulating, and communicating their employable skills to potential employers, with the broader goal of facilitating labour market integration for displaced and disadvantaged populations.

The core AI functionality of the application centres on an automated skills interview powered by natural language processing (NLP), graph theory, and Bayesian machine learning. The system is built on the European Skills, Competences, Qualifications and Occupations (ESCO) classification framework, which contains approximately 13,485 skills mapped to 2,942 occupations. During the profiling process, users engage with an AI-driven conversational interface that guides them through a structured self-assessment of their work history, education, and competencies. The underlying machine learning pipeline uses NLP and graph theory to exploit intrinsic relations within the ESCO skill dataset, a hybrid collaborative recommender system to intelligently query the most promising skills from the full ESCO taxonomy, and Bayesian machine learning algorithms that adapt to each user's input as the interview progresses. These Bayesian optimisation algorithms balance between exploiting the existing ESCO dataset and exploring possible new trends not captured in existing data. All machine learning components run on Google Cloud Platform and are written in Python.

At the conclusion of the profiling process, the application generates three key outputs for each user: a comprehensive list of their identified skills, a list of potential occupations matching those skills, and a professionally formatted CV that faithfully reflects the user's indicated skills, experience, and education. The system also assesses role fit by comparing individual skill profiles against occupation requirements, and identifies training gaps where additional skills development would enable access to further occupational pathways. Employment counsellors and career guidance staff use these outputs to inform their advisory sessions with job seekers, making the system an advisory and decision-support tool rather than one that makes autonomous placement or eligibility decisions.

The pilot was conducted among approximately 400 beneficiaries in the governorate of Alexandria, Egypt. ILO PROSPECTS Egypt subsequently integrated the application into Job Search Clubs (JSCs) operated in partnership with Caritas, establishing 16 JSCs between August 2021 and January 2022 in Alexandria and Damietta for asylum seekers and refugees. In the JSC deployment, 303 participants successfully used the skill profiling tool and extracted professional CVs.

A tracer study conducted by the ILO three months after the initial pilot (published in 2022) collected direct feedback from the 400 pilot beneficiaries. The study found that 64 percent of participants used the application to create a CV, 61 percent found it very easy or easy to use, and results on perceived usefulness for job search and training identification were mixed. In the subsequent JSC deployment, 76 percent of surveyed participants rated the application as good or excellent in assisting them to identify their skills, more than 75 percent found it good or excellent for CV writing, 64 percent discovered new potential employment fields matching their skills, and 78 percent found the application user-friendly. Four months after the JSC project concluded, 88 participants had found employment in occupations including marketing, sales, nursery teaching, electrician, tailor, and clinic assistant.

The human oversight model is explicitly human-in-the-loop (HITL). The application outputs serve as advisory inputs for employment counsellors, who retain professional judgement over career guidance recommendations. No automated entitlement, benefit, or placement decisions are documented. The ILO tracer study recommends localisation of terminology, induction training for new users, employer outreach to increase awareness of the skills profiles, and longer-term evaluation before wider roll-out.

SkillLab won the Google AI Impact Challenge in 2019, receiving financial support to further develop its AI-based skills assessment technology. The platform was recognised in the IRCAI Global Top 100 AI for Sustainable Development Goals index. The implementing model involves SkillLab as the third-party technology provider, with the ILO PROSPECTS programme funding the deployment and ABA handling local pilot delivery. The application targets refugees, migrants, mature workers, informal workers, and youth not in employment, education, or training (NEET).

Classification

AI Capabilities

Classification (primary)Perception and extraction from unstructured inputsRanking and decision systems

Use Cases

Matching and recommendation (primary)

Social Protection Functions

Implementation/delivery chain: Profiling, job matching and support services (primary)
SP Pillar (Primary)Labour market programmes

Programme Details

Programme NameILO PROSPECTS Egypt – Skills Profiling Application (SkillLab)
Programme TypeJob search assistance and placement services
System LevelImplementation/delivery chain

ILO PROSPECTS Egypt programme deploying SkillLab's AI-driven skills profiling application to assist refugees and host community members in Alexandria and Damietta in identifying their skills, generating professional CVs, and receiving career guidance aligned with local labour market opportunities. Piloted with the Association of Businessmen in Alexandria (ABA) and subsequently integrated into Job Search Clubs operated with Caritas.

Implementation Details

Implementation TypeClassical ML
Lifecycle StageIntegration and Deployment
Model ProvenanceCommercial/proprietary
Compute EnvironmentCommercial cloud
Compute ProviderGoogle Cloud Platform
Sovereignty QuadrantIV — Shared Innovation Zone
Data ResidencyNot documented
Cross-Border TransferNot documented

Risk & Oversight

Decision CriticalityLow
Human OversightHITL
Development ProcessFully third-party developed
Highest Risk CategoryModel-related risks
Risk Assessment StatusNot assessed

Risk Dimensions

Data-related risks

Representation bias

Governance and institutional oversight risks

Weak documentation or auditability

Market, sovereignty and industry structure risks

Upstream model or API dependencyVendor lock-in

Model-related risks

Reliability or generalisation failureSubgroup bias

Operational and system integration risks

Inadequate real-world validation

Impact Dimensions

Equality, non-discrimination, fairness and inclusion

Discriminatory outcomeSystematic exclusion from benefits or services

Systemic and societal

Deepened digital divide

Safeguards

Human oversight protocol

Deployment & Outcomes

Deployment StatusPilot / Controlled Trial Phase
Year Initiated2020
Scale / Coverage~400 beneficiaries in initial pilot (Alexandria); 303 additional participants in JSC deployment (Alexandria and Damietta)
Funding SourceILO PROSPECTS programme (funded by the Government of the Netherlands)
Technical PartnersSkillLab (Amsterdam, Netherlands) – provider of the AI-driven skills profiling application; Google AI Impact Challenge winner (2019).

Outcomes / Results

Tracer study (n=400): 64% used the app to create a CV; 61% found it very easy/easy to use; mixed perceived usefulness for job search and training identification. JSC deployment (n=303): 76% rated app good/excellent for skills identification; 75%+ good/excellent for CV writing; 64% discovered new employment fields; 78% found app user-friendly; 88 participants found employment within 4 months of project end.

Challenges

ILO tracer study recommends localisation of terminology, induction training for new users, employer outreach to increase awareness of skills profiles, and longer-term evaluation before wider roll-out. Mixed perceptions of usefulness for job search suggest need for stronger linkage to employer-side demand.

Sources

  1. SRC-003-EGY-001 IRCAI (n.d.) 'SkillLab', IRCAI Global Top 100 Outstanding Projects. Available at: https://ircai.org/top100/entry/skilllab/ (Accessed: 24 March 2026).
    https://ircai.org/top100/entry/skilllab/
  2. SRC-001-EGY-001 International Labour Organization (2022). Tracer study: Skills Profiling Application in Egypt for refugees and host communities. Geneva: ILO PROSPECTS. Available at: https://www.ilo.org/publications/tracer-study-skills-profiling-application-egypt-refugees-and-host (Accessed 31 Oct 2025).
    https://www.ilo.org/publications/tracer-study-skills-profiling-application-egypt-refugees-and-host
  3. SRC-002-EGY-001 International Labour Organization (2022). Tracer study: Skills Profiling Application in Egypt for refugees and host communities – Knowledge Product page. Geneva: ILO Skills and Lifelong Learning Platform. Available at: https://www.skillsforemployment.org/knowledge-product-detail/5889 (Accessed 31 Oct 2025).
    https://www.skillsforemployment.org/knowledge-product-detail/5889
  4. SRC-005-EGY-001 International Labour Organization (n.d.) 'Assisting refugees and host communities in Egypt to turn their skills into careers'. Geneva: ILO PROSPECTS. Available at: https://www.ilo.org/global/programmes-and-projects/prospects/countries/egypt/WCMS_779051/lang--en/index.htm (Accessed: 24 March 2026).
    https://www.ilo.org/global/programmes-and-projects/prospects/countries/egypt/WCMS_779051/lang--en/index.htm
  5. SRC-004-EGY-001 SkillLab (n.d.) 'Empowering Young Refugees to Find Decent Work in Egypt'. Amsterdam: SkillLab. Available at: https://skilllab.io/en-us/news/ilo-caritas-refugees-egypt (Accessed: 24 March 2026).
    https://skilllab.io/en-us/news/ilo-caritas-refugees-egypt

How to Cite

DCI AI Hub (2026). 'ILO PROSPECTS Egypt – Skills Profiling Application (SkillLab)', AI Hub AI Tracker, case EGY-001. Digital Convergence Initiative. Available at: https://socialprotectionai.org/use-case/EGY-001

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