Publication Date
| In 2025 | 2 |
Descriptor
| Automation | 2 |
| Test Reliability | 2 |
| Artificial Intelligence | 1 |
| Attribution Theory | 1 |
| Computer Assisted Testing | 1 |
| Decision Making | 1 |
| Evaluation Methods | 1 |
| Formative Evaluation | 1 |
| Grading | 1 |
| High Stakes Tests | 1 |
| Scores | 1 |
| More ▼ | |
Author
| Bin Tan | 1 |
| Elisabetta Mazzullo | 1 |
| Jinnie Shin | 1 |
| Mark J. Gierl | 1 |
| Nour Armoush | 1 |
| Okan Bulut | 1 |
| Wallace N. Pinto Jr. | 1 |
Publication Type
| Journal Articles | 2 |
| Information Analyses | 1 |
| Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textBin Tan; Nour Armoush; Elisabetta Mazzullo; Okan Bulut; Mark J. Gierl – International Journal of Assessment Tools in Education, 2025
This study reviews existing research on the use of large language models (LLMs) for automatic item generation (AIG). We performed a comprehensive literature search across seven research databases, selected studies based on predefined criteria, and summarized 60 relevant studies that employed LLMs in the AIG process. We identified the most commonly…
Descriptors: Artificial Intelligence, Test Items, Automation, Test Format
Direct linkWallace N. Pinto Jr.; Jinnie Shin – Journal of Educational Measurement, 2025
In recent years, the application of explainability techniques to automated essay scoring and automated short-answer grading (ASAG) models, particularly those based on transformer architectures, has gained significant attention. However, the reliability and consistency of these techniques remain underexplored. This study systematically investigates…
Descriptors: Automation, Grading, Computer Assisted Testing, Scoring
