Practical experiences of artificial intelligence in science clubs
Bibliographic record
Abstract
The 21st century presents us with knowledge and technologies like AI introducing new educational possibilities to improve human talent and performance. In recent years there has been an increase in the literature on artificial intelligence in education and research opportunities with practical experiences in communities in emerging countries. This study investigates how participation in science clubs focused on AI-related projects supports the development of complex thinking and scientific thinking among high school and university students. Drawing on a multiple case study design, the research analyzes six cases involving 83 students across four Mexican cities, all engaged in science clubs jointly organized by academic teams from Mexico and the United States. The results show that (a) participants in all analyzed cases of practical applications of AI demonstrated a high perceived level of complex thinking competency; (b) although no statistically significant differences were found, women, on average, tended to report slightly higher perceptions of complex thinking development compared to their male counterparts; and (c) a similar non-significant trend was observed for scientific thinking, with women reporting marginally higher self-perceived levels than men. These insights contribute to the global conversation on integrating AI into non-formal education to cultivate transversal cognitive skills applicable across diverse educational contexts.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".