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Good practice Imported

AI-for-Equity — Cross-Border AI and Coding Mentorship for Girls in Dhaka

Bangladesh · Dhaka · See the Bangladesh profile · See the Dhaka profile

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

Since 2019, nonprofit AI-for-Equity has run online coding and AI-ethics classes pairing Dhaka girls (15+) with US student mentors; independent reporting cites 60-80% of university-bound graduates later choosing STEM fields.

20 girls (approx.)
Founding cohort size (2019)
60-80 %
Graduates pursuing STEM at university
2 %
Women in Bangladesh with foundational ICT skills
AI-for-Equity — Cross-Border AI and Coding Mentorship for Girls in Dhaka

Details

Maturity
Pilot
Promoter
AI-for-Equity (AIE)
Period
2019–2023 (ongoing)
Keywords
AI education, gender equality, mentorship

Context

AI-for-Equity (AIE), founded by data scientist Farhana Faruqe, runs a free, online AI and coding programme for girls in Bangladesh coordinated from Dhaka, starting with a first cohort of roughly 20 girls aged 15 and up in 2019, reached originally through a private girls' school.

Activities

The programme pairs Bangladeshi students with peer mentors in the United States across a 12-hour time difference, teaching MIT's Scratch before moving into Python and applied AI concepts, with AI ethics integrated throughout rather than taught as an add-on, and requires no prior technical background.

Results

Independent reporting by the University of Virginia's School of Data Science - not AIE's own materials - cites that 60-80% of programme graduates who went on to university chose to pursue STEM fields, against a backdrop in which only 2% of women in Bangladesh have foundational ICT skills.

Conclusions

No independently described evaluation with a matched comparison group was found, so the STEM-uptake figure is best read as a self-reported alumni outcome relayed through independent journalism rather than a formal impact study, and the programme continues despite logistical constraints such as students relying on parents' shared laptops.

Implementation
Indicative cost
Low (< €50k)
Time to results
Long (> 3 years)
Staffing & skills
Founded and run by data scientist Farhana Faruqe, with volunteer peer mentors based in the United States paired with Bangladeshi students.

Conditions for success

  • A peer-to-peer, cross-border mentorship model requiring no prior technical background from participants.
  • AI ethics integrated throughout the curriculum rather than taught separately.

Common failure modes

  • No independent, methodologically described evaluation (e.g., a matched comparison group) was found.
  • The programme runs despite participants relying on parents' shared laptops, a logistical constraint on scale.

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Data sources

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

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