Aligning policy and practice: The World Bank’s approach to EdTech in Sub-Saharan Africa
Bibliographic record
Abstract
This study uses a case study approach to examine the World Bank’s policy prescriptions and funding of ICT in education from 2011 to 2022. Through document analysis of the Bank’s research, formal policy documents, and its portfolio of projects in K-12 education in Sub-Saharan Africa (SSA), the findings reveal a shift in the Bank’s educational technology (EdTech) strategies. Prior to the pandemic, the Bank’s EdTech policies and projects centered on system-level solutions: focusing on infrastructure, management, and monitoring, particularly in low-income countries. However, during the COVID-19 pandemic, the Bank shifted its focus to instructional solutions, emphasizing curriculum development, pedagogy, and equity, promoting “multimodality,” defined as the use of diverse ICT tools to support remote learning. While the pandemic has led to greater alignment between policy advice and investments in SSA, the long-term sustainability and equitable distribution of these investments remain uncertain. Further studies should assess the enduring impacts of the Bank’s EdTech approach on borrowing countries and further explore how the Bank’s EdTech strategies compare to other international organizations, as well as the role of civil society and private technology firms in shaping a more inclusive educational landscape. Moreover, emerging technologies, such as Artificial Intelligence (AI) and blockchain, present new opportunities. Investigating how these technologies could enhance educational equity, efficiency, and innovation within the World Bank’s evolving EdTech framework will be vital for shaping sustainable, future-oriented policy recommendations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".