El proceso de enseñanza-aprendizaje a prueba en la era de la integridad académica (IA1) x inteligencia artificial (IA2)
Bibliographic record
Abstract
While Higher Education institutions aim students to develop the concept of academic integrity (AI1), the arrival of artificial intelligence tools (AI2) like ChatGPT puts the process of teaching-learning to test. Respective to teachers, they must inculcate ethical values while students are confronted with a great deal of factors that influence them during their academic writings. Given this situation, the main objective of this article is to determine the competencies that must be developed in this era of continued immersion in AI2 to guarantee AI1. To do so, the results of a quantitative descriptive analysis of the relevant sections and questions from questionnaires of the faculty (1357 participants) and the students (4664 participants) who participated in this first data collection of the study by Partnership on University Plagiarism Prevention (PUPP), conducted in 2023, will be used to apply them to the discipline of Spanish as a foreign language (SFL). The conclusion will be that, to accomplish their respective tasks, it is necessary that faculty and students work hand in hand.
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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 | 0.011 | 0.017 |
| 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 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".