Decision-making criteria for AI tools in digital education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial intelligence (AI) technologies in education have great potential, but choosing the right ones necessitates using well-informed selection criteria. Drawing on studies over the last five years, this review investigates important factors to consider when educators choose AI tools. The impact on motivation and knowledge enhancement using quasi-experimental approaches, prediction accuracy utilizing machine learning models and cross-validation procedures, and algorithm performance (e.g., accuracy, precision, recall) are some of the key criteria that were discovered. Fairness, transparency, and gender prejudice are important ethical considerations that call for creating policy frameworks to reduce bias and uphold ethical integrity. Along with concerns about educational equity and the caliber of AI-generated content for tailored learning experiences, transparency in AI operations is found to be essential for acceptability. The analysis highlights prospect to improve educational results while addressing ethical and practical constraints by synthesizing studies to emphasize the systematic evaluation required for AI tool use in education.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it