A Evaluation of AI-Driven Learning Strategies and Business Innovation for SDG Dissemination in Meta Colombia
Bibliographic record
Abstract
The problem encountered is the deficient knowledge about the Sustainable Development Goals (SDG) adopted by the United Nations as part of the 2030 agenda in Mesetas and Lejanías, in Meta, Colombia. The problem was solved by sharing at the local level a learning strategy with Artificial Intelligence (AI) and emerging technologies for sustainable innovation with the participation of undergraduate students levels 10 and 11, small business agricultural entrepreneurs and rural producers from the localities of study, useful for the dissemination of the SDGs in the rural sector of the Ariari in Colombia using as a model a successful sustainable business that obtained inherent results to the evaluation of the effectiveness of techniques for the dissemination of knowledge, attitudes and practices as well as metacognitive strategies and AI at the local level useful for the dissemination of the SDGs in Meta Colombia. The data obtained in the research imply effective support strategies for self-assessment and practice for consolidation of learning about the SDG for rural communities using AI. The findings show that AI-powered systems increase learner motivation, engagement, and knowledge retention while providing scalable solutions for various educational scenarios. Limitations include the scope of the implementation and the necessity for additional quantitative study. The paper continues by identifying areas for further research and practical implementation tactics and establishing significant contribution from AI in rural education about the SDGs.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".