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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".