Education.
Follow enrollment and learning histories.

FCTE designs education as a longitudinal part of a citizen's life — from school-age eligibility and enrollment through progression, attainment and exit — connected to the same synthetic citizens, households and geography used everywhere else in the City Twin™.

Nairobi City Twin™ · 1960–2026

See One Education History above for how this chain was validated.

Five real, published Enrollment rows, randomly sampled — full 100-row CSV available below. No record on this page represents a real student.

Download 100-Row Research Sample (CSV) →

View Education Deployment Status →

Connections follow the published domain scope. Optional, contextual and planned relationships retain their stated limits below.

FCTE's Education model represents a full learning history for a synthetic resident — not one enrollment flag, but a chain of dated, era-aware learning events from school-age eligibility through exit. The cards below describe the public semantic capabilities scoped for Nairobi.

Enrollment eligibility is gated on the same DOB used everywhere else in the City Twin™ — a citizen becomes eligible for a stage as they age, not on a fixed generation date.

Enrollment context follows each synthetic resident by academic year, education era and institution, with release checks preventing incompatible duplicate enrollment states.

Progression advances year by year through the applicable education stages, with examination context marking important transition points.

Attainment context preserves the highest supported qualification state a synthetic resident has reached.

Exit context records how an enrollment spell ended — for example graduation, transfer, dropout, death, financial interruption or another modeled reason.

School context is anchored to the same public administrative geography used across the City Twin. Tertiary geography is currently less granular, and that limitation is disclosed below.

Synthetic by design, live on Nairobi: the education model is populated — 9,266,175 Enrollment rows across 2,060 schools, 80,000 teachers and 10,004,000 Attendance rows. Public pages describe entities, historical context and certification boundaries without publishing the physical constraint graph or generation methods. No record on this page represents a real student, school or transcript.

Every stage below is live and populated on Nairobi — the tags mark code status, not population status.

Female, DOB 1992-06-08, Nairobi. Six real, published Enrollment rows spanning 1995–2009 — not a continuous year-by-year progression, but independent per-year eligibility draws, which is why the gaps between years are real, not missing data.

Two consecutive Nursery-year enrollment records, 844 system (Kenya's pre-CBC 8-4-4 curriculum).

Re-appears three years later — the independent per-year draw skipped 1997–98 for this citizen.

A four-year gap, then re-enrolled at the grade her age implies under the 844 system.

Her most recent records. No ExitReason is populated — honest, since only 2.2% of all 2.2M Nairobi Enrollment rows have one ("Graduated"); most enrollment spells, including this one, simply have no recorded exit.

View the Underlying Records →

Demographics tells us who the citizen is. Households show where they grow up. Education is designed to show how schooling, attainment and transitions relate to later employment, income potential, migration and other life outcomes — without claiming the engine currently models those effects.

FCTE is built so it can preserve the relationship between education and later domains, because the same citizen identity persists across the City Twin™. Whether it actually does today depends on what's been generated and what's been coded to depend on what — and right now, the honest answer is: neither has happened yet.

Education and later-life domains share the same persistent synthetic-resident identity, so longitudinal histories can be compared without publishing the physical key or join structure.

The current release does not use education attainment to causally alter employment outcomes, and employment does not feed back into education outcomes. No such effect is claimed.

Education does not change employment probability, occupation assignment or earnings for any synthetic citizen. A correlational reference is designed for the future — measuring co-occurrence between education and employment records — but no causal effect is modeled or claimed today.

Education history and employment history are connected at the citizen level; causal influence is shown only where explicitly modeled. The page will not say "Education determines income" until the engine actually implements that dependency.

Nairobi' data structures encode real, era-specific education-system history via a checked EraCode / EducationSystem field — not a single flat curriculum label. No historical rows have been generated yet; only the era classification itself exists today.

Enrollment, attendance and examination history are live and span these eras — 9,266,175 Enrollment rows and 10,004,000 Attendance rows on Nairobi, back to 1960.

Direct education records are live on Nairobi — 9,266,175 Enrollment rows, 80,000 Teacher rows, 10,004,000 Attendance rows.

Measures calculated from the live Enrollment/Attendance/Examination base — see the Reality Check below for current results.

Enrolment, progression and attainment patterns are calibrated to cited education evidence and checked at release level. Exact weights and transition thresholds remain controlled.

Individual synthetic assignments are generated under controlled domain rules. Exact sampling, assignment order and parameterization are not published.

View Education Generation Methodology →

Primary, secondary and tertiary participation — live and populated: 9,266,175 Enrollment rows, 3,109 TertiaryEnrollment rows.

Highest completed level and result distribution, via ResultBand and FinalGrade enumerations.

Progression and exit evidence, via the checked ExitReason enumeration (Graduated / Transferred / Dropout / Death / Financial / Other).

Institution types, education eras and jurisdictional context represented through an era-aware education-system model.

A catalogued external source for Education is still in progress. Other domains cite published national survey sources directly; Education's citation set is not published yet, and we won't invent one in the meantime.

Open the Source Catalogue →

The publisher validates Nairobi’s new age and duration reporting. Grade-year coverage still has 3 incomplete cells, and EDU-FIND-06/07 remain open.

Reported city totals: 4,276,253 active enrollments; 812,986 active for over 20 years and 1,647,542 for over 10 years. These are city-wide counts, separate from the selected demo. The supplied duration subtotals reconcile; complete band tables were not supplied for independent review.

Age and duration checks are informational or diagnostic. Age cutoffs are not hard failure gates without external grounding.

EDU-FIND-03 remains closed based on the earlier publisher validation. Its reported 18/18 result describes that earlier run and does not supersede the later open findings.

Current publisher review: 3 grade-year cells remain incomplete, and terminal enrollment findings remain open. Long-running active records are preserved as source states; they do not prove continuous attendance. The earlier enrollment/death timing closure does not make Education fully clear.

An earlier guide recorded 3,221,118 attendance rows before enrollment started. The published 3 September 2026 attendance-start check reports zero violations. These results describe different versions; the later enrollment/death closure does not provide a separate attendance reconciliation.

Synthetic schools do not represent real student rosters or actual school populations. Secondary/primary schools are anchored to a real ward; tertiary institutions (universities, polytechnics, TVET colleges) are not ward-anchored in the current design.

Enrollment or attainment does not automatically imply educational quality, learning outcomes or literacy proficiency — none of those are claimed by the current public model.

Education is not presented as causing employment, income or health outcomes. That dependency is not modeled today — see the Later-Life Connection section above.

View All Education Limitations →

The following results belong to the earlier published guide. Current open findings are listed in the Education review above.

Three realism dimensions are defined, sourced and run live against Nairobi's populated Enrollment/Attendance/Examination base — not a placeholder pass.

A correlational reference comparing employment rates among adults who completed an education record against a national benchmark is live above (Employed-Adult Enrollment Coverage). Full realism report: see the link below.

Open the Education Realism Report →

The full 13-check structural battery plus a 6-check realism pass run live against the Nairobi release. All 13 structural checks pass.

Explore Every Domain's Certification Status →

• certification check — Schools populated (2,060), Enrollment/Teacher citizen relationship integrity — PASS
• certification check — Enrollment age plausibility, EnrollmentDate within SeedEpoch–NowDate — PASS
• certification check — Teacher licence uniqueness, TertiaryEnrollment year order, ward school coverage — PASS
• certification check — Attendance date ≥ Enrollment start — PASS, 0 rows

• certification check — EnrollmentDate within SeedEpoch–NowDate — PASS
• certification check — TertiaryEnrollment end year ≥ start year — PASS
• certification check — Attendance date ≥ Enrollment start — PASS, see Structural

• certification check — Enrollment within plausible age band — PASS, 0 out-of-band rows
• certification check — Employed-adult enrollment coverage vs. NBF R-03 — PASS, 100.00%
• certification check — School tier enrollment share vs. the school-tier calibration target — PASS, 1.21pp deviation
• certification check — Program length plausibility — PASS, 0 implausible rows

• certification check — TertiaryInstitution/StudentPopulation ward & university relationship integrity — PASS
• Education and Employment share persistent resident references. A causal effect of education on employment outcomes is not included in this release.

A synthetic resident enters the school-age window according to lifecycle context.

Enrollment is generated and certified across 2,060 Nairobi schools, gated on the same DOB used everywhere else in the City Twin™.

Progression through education stages is populated longitudinally, 1960–2026.

Attainment and exit context are part of the populated model, alongside 60,000 Bursary and 1,383 TertiaryEnrollment rows.

The same resident can appear in Employment. A causal effect of education on job and salary outcomes is not included in this release.

Education history sits alongside household, health, transport and economic context through the shared resident identity.

Available as Standalone Domain · Multi-Domain Bundle · Full City Twin™