AI for Sustainable Development in the Belt and Road Countries - Need for Capacity Building
Bibliographic record
Abstract
All United Nations member countries are striving to achieve the Sustainable Development Goals (SDGs), with information and communication technologies (ICTs) playing a crucial role in accelerating the progress. However, there is a significant disparity in technological capacities between the least developed, developing, and developed nations. Leading countries maintain dominance in these technologies, creating a digital divide that redefines richness and poverty. The Belt and Road Initiative (BRI), launched by China in 2013, offers a beacon of hope for developing nations. Over the past decade, China has introduced the Global Development Initiative (GDI), Global Security Initiative (GSI), and Global Civilizations Initiative (GCI) under BRI, benefiting many developing countries. Despite these advancements, many BRI member countries lack the capacity to fully utilize these technologies and maintain the infrastructure so developed by China. There is an urgent need for education and capacity building to ensure these nations can leverage AI and ICTs for sustainable development. Artificial Intelligence (AI) is transforming education by offering personalized learning experiences, optimizing administrative processes, and facilitating data-driven decision-making. AI-powered educational tools can adapt to individual student needs, enhancing engagement and learning outcomes. However, transitioning from traditional to technology-driven education systems requires robust capacity-building efforts to equip educators and institutions with the necessary skills and knowledge. Collaboration between governments, academia, industry, and civil society is essential for driving innovation and integrating AI responsibly and inclusively in education. This paper highlights the need for capacity building, particularly for students at all educational levels, to prepare them for future challenges and to achieve the SDGs through AI and technology-driven education.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".