Implementación de la inteligencia artificial en la educación superior: el caso de la Universidad Francisco Gavidia
Bibliographic record
Abstract
This research project aimed to develop and implement a holistic framework that integrated artificial intelligence (AI) tools into the educational processes of Universidad Francisco Gavidia (UFG), with the purpose of analyzing the optimization of academic planning for faculty, improving the teaching–learning experience, and assessing its impact on students from the Faculty of Engineering and Systems and the Faculty of Social Sciences. For its implementation, UFG collaborated with the Argentine company Evaluados Ai, utilizing both the technological tools developed by the company and its expertise in teacher training. The project included the use of AI agents, among them the RP-02 assistant, a tool specifically designed to facilitate academic planning. Likewise, it supported key processes such as the creation of unit-based planning matrices, the definition of session purposes, recommendations for didactic strategies, assessment methods, and complementary resources. This contributed to a significant reduction in the time faculty members devoted to these tasks, allowing them to focus on more strategic pedagogical activities. The research adopted an iterative approach, evaluating the impact of these tools in real educational settings through both qualitative and quantitative methodologies. The results demonstrated improvements in operational efficiency, greater user acceptance of the technology, and more personalized and effective learning experiences. This project aligned with global trends in educational innovation and represented a concerted effort to position UFG as a leader in the integration of emerging technologies in higher 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 | 0.006 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".