Transforming Mining Education in Kenya: Bridging Skills Gaps for Technological Readiness and a Sustainable Future
Bibliographic record
Abstract
Kenya’s mining sector holds significant potential to contribute to economic diversification and sustainable development, yet its growth is constrained by a persistent mismatch between educational output and industry demands. This paper examines the state of mining education in Kenya, identifying key skills gaps that limit technological readiness and hinder the adoption of sustainable mining practices. Using a traditional (narrative) literature review approach, the study synthesizes findings from academic and policy sources to provide a comprehensive understanding of current challenges and opportunities. The review reveals that mining education remains largely traditional, with outdated curricula, limited access to modern technologies, and weak linkages between academia and industry. These gaps have led to a shortage of competent professionals capable of operating within a rapidly digitalizing and sustainability-driven global mining environment. Drawing insights from international best practices in countries such as Australia, South Africa, and Canada, the paper highlights the need for curriculum reform, enhanced industry partnerships, investment in technological infrastructure, and the integration of sustainability principles into all aspects of mining education. The findings emphasize that transforming Kenya’s mining education system is essential for preparing a workforce that is technologically competent, environmentally conscious, and aligned with the nation’s Vision 2030 and the United Nations Sustainable Development Goals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".