Blockchain and Artificial Intelligence Non-Formal Education System (BANFES)
Bibliographic record
Abstract
The resurgence of the Taliban in Afghanistan has significantly exacerbated educational challenges for marginalized women and girls, deepening gender disparities and impeding socio-economic development. Addressing these issues, this article introduces the Blockchain and Artificial Intelligence Non-Formal Education System (BANFES), an innovative educational solution specifically designed for Afghan girls deprived of formal schooling. BANFES leverages advanced artificial intelligence technologies, including personalized data analysis, to provide customized learning experiences. Additionally, blockchain technology ensures secure record management and data integrity, facilitating a decentralized educational ecosystem where various nodes offer hybrid learning methodologies without intermediaries. This system not only adapts to individual learning speeds and styles to enhance engagement and outcomes but also employs an independent assessment mechanism to evaluate learners. Such evaluations promote transparency and maintain the quality and reputation of educational contributions within the network. The BANFES initiative also addresses implementation challenges, including local distrust and integration with existing educational structures, providing a robust model to overcome barriers to education. Furthermore, the paper explores the scalability of BANFES, proposing its application as a global strategy for non-formal education systems facing similar geopolitical and infrastructural challenges. By creating a secure, flexible, and learner-focused environment, BANFES aims to empower Afghan women and girls with essential skills for personal and professional growth, thus fostering socioeconomic advancement within their communities and setting a new standard for informal education worldwide.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".