Artificial Intelligence application in Education
Bibliographic record
Abstract
This research aimed to examine the potential of artificial intelligence (AI) for use in higher education and to assess the consequences of using AI in this setting. The research uses demographics like age, gender, and field of study might affect the implementation of AI in classrooms using analysis of variance or ANOVA methodology. The study included 209 participants, or 52.2% of the population, with 100 male and 109 female participants. In the results there was a large age and major-related divide in how respondents used AI in the classroom. Younger respondents were more likely to indicate extensive use of AI in the classroom. Neither men nor women reported significantly different rates of AI use in the classroom. Another interesting finding is that respondents enrolled in STEM-related programs were more likely to use AI in the classroom than those enrolled in other programs. Based on these results, age and field of the study appear more influential than gender when it comes to the application of AI in the classroom. This research has the potential to inform the creation of policies and tactics that will increase the prevalence of AI in classrooms across all ages and subject areas. Due to its limited sample size and focus on a single institution, the University of Lima in Peru, the study has certain caveats.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".