Editorial: Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement (Part 3)
Bibliographic record
Abstract
This editorial introduces the third and final installment of the Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement. Building on the first two parts, which explored AI-driven innovations in assessment design, scoring, learning analytics and their ethical challenges, this issue features two papers that offer timely and extensive insights into the integration of natural language processing (NLP) and generative language models in educational measurement --- a tutorial on building an NLP pipeline in R for analyzing constructed-response data, and a systematic review of generative language models for automated writing evaluation. With this third installment, we bring to a close the Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement. Across all three parts, the collected contributions have demonstrated both the remarkable promise and the profound challenges that accompany the infusion of AI and ML into assessment research and practice. As AI continues to transform our field rapidly, we hope this special issue will inspire researchers, practitioners, and policymakers to work toward a better future in which AI-powered assessment practices are technologically advanced, ethically sound, and fully supportive of diverse learners.
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 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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.066 | 0.039 |
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".