From routine to creativity: how artificial intelligence is transforming the work of educators
Bibliographic record
Abstract
The process of creating assessment materials is one of the most labor-intensive and routine tasks in an educator's work. Searching for and adapting questions, ensuring their uniqueness, matching various student proficiency levels, as well as the constant need to update and store test materials — all these consume significant time and effort. Many teachers face challenges in scaling tests for large student groups, maintaining objectivity in evaluation, and keeping assignments relevant amid rapidly changing educational and professional standards. Modern artificial intelligence technologies offer solutions to these problems. The automatic question generation system developed by the Artificial Intelligence in Industry Laboratory at Tomsk Polytechnic University is a tool that not only simplifies the test creation process but also significantly improves the quality and efficiency of asses- sments.
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.013 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".