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Good practice Imported

Girls Code Mongolia — AI Academy Asia's Coding and AI Curriculum for Nomadic and Disadvantaged Girls

Mongolia · Ulaanbaatar · See the Mongolia profile · See the Ulaanbaatar profile

Evidence: Descriptive / self-reported Top 40% 53/100 · Ask Evidence Copilot about this practice

Founded in 2021 by Bolor-Erdene Battsengel, Girls Code has trained 110-120 Mongolian girls aged 14-18 from nomadic and disadvantaged communities in coding and AI via AI Academy Asia's curriculum; 90% go on to STEM degrees, including admissions to Harvard, MIT and Cambridge.

110-120
Girls graduated from the programme (2021-2025)
90 %
Graduates advancing to STEM degree majors
500 teachers
Planned rural teacher-training reach (AI Academy Asia)

Details

Maturity
Scaling
Promoter
Girls Code (Nomadic Girls Association in the Digital Age) / AI Academy Asia
Period
2021-2025
Keywords
AI literacy, coding, girls' education, digital and disability inclusion

Context

Girls Code was founded in Mongolia in 2021 by Bolor-Erdene Battsengel, later Mongolia's State Secretary for Digital Development, to give girls aged 14-18 from nomadic herding and other disadvantaged communities a route into coding and, through the affiliated AI Academy Asia curriculum, applied AI skills.

Objectives

The programme responds to a stark rural-urban digital divide, aiming to give girls who grow up moving with livestock across the steppe, far from Ulaanbaatar's schools and internet access, a pathway into coding, AI and English/communication skills.

Activities

Training combines coding fundamentals, AI Academy Asia's AI modules, and English/communication skills, delivered through bootcamps and ongoing mentoring, with an explicitly disability-inclusive design illustrated by a hearing-impaired 15-year-old participant supported by a sign-language-fluent peer mentor.

Results

As of 2024-2025 reporting, more than 110-120 girls have graduated, and Girls Code and independent press (World Economic Forum, CNN Business) report that 90% of graduates go on to STEM majors, with some admitted to Harvard, MIT and Cambridge on scholarship.

Conclusions

These outcome figures come from the NGO and its press coverage rather than an independent, peer-reviewed evaluation, and the programme's reach of roughly 100-120 girls to date is small relative to Mongolia's national student population, with no control group or comparison cohort published.

Implementation
Indicative cost
Medium (€50k–€500k) — NGO-run bootcamps and ongoing mentoring for a cohort in the low hundreds imply a medium, sustained operating cost rather than a one-off low-cost intervention.
Time to results
Long (> 3 years) — Running continuously since 2021 with a growing annual graduate cohort, indicating a long-running, ongoing programme rather than a short pilot.
Staffing & skills
Girls Code founder Bolor-Erdene Battsengel, AI Academy Asia curriculum team, peer mentors, including a sign-language-fluent mentor for disability inclusion

Conditions for success

  • targeted recruitment of girls from nomadic herding and disadvantaged communities
  • disability-inclusive design with peer mentoring
  • a combined coding, AI and English/communication curriculum delivered via bootcamps and ongoing mentoring

Common failure modes

  • reach remains small (roughly 100-120 girls) relative to Mongolia's national student population
  • outcome figures are NGO- and press-reported rather than independently evaluated

Commonly funded by

Philanthropic / foundation funding

Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.

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Data sources

Where this practice's information was retrieved from, and when.

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