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Good practice Imported

ENGAGE — Enabling Girls in AI and Data Science, Kenya's Public-Health-Focused AI Training Pathway

Kenya · Nairobi · See the Kenya profile · See the Nairobi profile

Evidence: Descriptive / self-reported Top 59% 47/100 · Ask Evidence Copilot about this practice

ENGAGE trains 800+ underserved Kenyan girls and young women in AI, data science and machine learning for public health, through a tiered pathway from secondary school to advanced research, backed by a $5.5M Takeda-funded UCSF–University of Nairobi partnership.

480 participants
Secondary-school girls in introductory tier (2025–)
270 participants
Young women in intermediate STEM-diploma tier (2025–)
135 participants
Women in advanced data-science/AI/ML tier (2025–)
5.5 million USD
Total Takeda-funded award (via University of Nairobi) (2025–)
1 million USD
UCSF Institute for Global Health Sciences contract (2025–)

Details

Maturity
Pilot
Promoter
University of Nairobi (UNITID), CEMASTEA & UCSF Institute for Global Health Sciences
Period
2025–
Keywords
STEM education, gender equity, data science, public health

Context

ENGAGE (Enabling Girls in AI and Growing Expertise) is a data-science and AI training pathway for underserved Kenyan girls and young women, led by the University of Nairobi's Institute of Tropical and Infectious Diseases (UNITID) with CEMASTEA, the UCSF Institute for Global Health Sciences, and a consortium of Kenyan universities. It targets Form Three secondary-school girls with strong mathematics aptitude across six, primarily rural, regions, including a Teachers Service Commission-backed cohort in Migori County.

Objectives

The programme aims to build a tiered pipeline of AI and data-science skills for public-health research among girls and young women who are otherwise underrepresented in STEM, moving participants from an introductory secondary-school level through STEM diploma training to advanced research training.

Activities

Training is delivered in three tiers: 480 secondary-school girls receive an introduction to AI and data science; 270 young women pursuing STEM diplomas receive intermediate training; and 135 women receive advanced training in data science, machine learning and AI for public-health research. The UCSF Institute for Global Health Sciences received a $1 million contract through the University of Nairobi as part of an approximately $5.5 million award from Takeda Pharmaceuticals.

Results

As of publication, programme materials describe participant numbers (800+ girls and young women across the three tiers) and curriculum design in detail, but do not yet report learning-outcome or skills-assessment data, since the tiered training was still being rolled out.

Conclusions

ENGAGE demonstrates a structured, multi-institution funding and delivery model for scaling AI/data-science training to underserved girls, but its actual learning impact remains unmeasured pending completion of the rollout.

Implementation

Indicative cost
High (€500k–€5M)
Time to results
Medium (1–3 years)
Staffing & skills
University of Nairobi Institute of Tropical and Infectious Diseases (UNITID), Centre for Mathematics, Science and Technology Education in Africa (CEMASTEA), UCSF Institute for Global Health Sciences, Consortium of Kenyan universities (Kabarak, JOOUST, Dedan Kimathi, Meru, Pwani, South Eastern Kenya University), Teachers Service Commission (Migori County cohort)

Conditions for success

  • Multi-institution partnership spanning a lead university, a teacher-education centre and an international academic funder/partner
  • Secured multi-year philanthropic funding (~$5.5M Takeda award) before scaling delivery
  • Government (Teachers Service Commission) co-endorsement for at least one regional cohort
  • Targeting participants by demonstrated maths aptitude rather than open enrolment

Where it fits

Governance type
university-led with government and philanthropic partners
Scale
regional (six regions of Kenya)
Income level
lower-middle-income

Commonly funded by

Philanthropic / foundation funding National / regional programmes

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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