A Review of Automatic Item Generation Techniques Leveraging Large Language Models
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
Over a decade ago, automatic item generation (AIG) was introduced to meet the increasing need for high-quality items in educational measurement. Around the same time, a new area of research in computer science began to develop questions for educational use. Historically, researchers from these two domains had little knowledge or communication with one another. However, the development of pre-trained large language models (LLMs) has sparked the interest of researchers from both domains in applying these models for automatically creating items. With similar objectives and methodologies, these two research domains appear to be converging on how to address the problems in this field. The purpose of this study is to provide a review of the current state of research by synthesizing existing studies on the use of LLMs for AIG. By combining research from both domains, we examine the utility and potential of LLMs for AIG. We performed a comprehensive literature review in 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 used LLMs in current AIG literature, their specific applications in the AIG process, and the characteristics of the generated items. We found that LLMs are flexible and effective in generating various types of items based on different languages and subject domains. However, many studies have overlooked the quality of the generated items, indicating a lack of a solid educational foundation. This review emphasizes the urgent need for greater integration of learning and measurement theories in future AIG research. We share two suggestions to enhance the educational foundation for leveraging LLMs in AIG, advocating for interdisciplinary collaborations to exploit the utility and potential of LLMs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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