Integración de la inteligencia artificial y la educación superior: nuevas dimensiones en la experiencia universitaria
Bibliographic record
Abstract
The integration of artificial intelligence in higher education represents an unprecedented opportunity to transform traditional teaching and learning paradigms. This research aimed to explore and develop AI applications that improve educational interaction, personalize learning, and increase the efficiency of academic processes. As a result, Evaluados Ai, a platform designed to optimize the planning and creation of educational resources at the university level, was implemented. The methodology was iterative in nature. It began with a preliminary study to identify areas with the greatest potential for impact, followed by the development of AI solution prototypes that were tested and validated in real educational contexts by the university's research team. The evaluation was conducted using qualitative methodologies focused on measuring the effectiveness of the technological intervention. Among the main results are reports on the implementation and effectiveness of the developed solutions, case studies and a series of recommendations for the integration of AI in higher education. Two use cases were documented: a wizard for the creation of adaptive learning objects and another for detailed planning by unit of study, both oriented to respond to specific needs of the subjects taught. This study allowed the development and validation of AI tools that improve efficiency and personalization in the planning and creation of educational resources, which represents a significant advance for higher education institutions.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".