Decision-making criteria for AI tools in digital education
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.
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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 | 0.067 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".