Reading Comprehension Quiz Generation using Generative Pre-trained Transformers
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
Recent advances in AI have resulted in large pre-trained language models with superior performance on text generation tasks, prompting the question of whether we can use them to generate educationally useful text completions. This holds the potential to generate relevant quizzes for any educational text, greatly complementing current formative and summative tests from education professionals. We explore pre-trained language models for quiz generation on reading comprehension texts and propose EduQuiz, an end-to-end quiz generator based on a GPT-3 model fine-tuned on text-quiz pairs, able to generate a complete multiple-choice question, with the correct and distractor answers. We observed that the majority of generated quizzes is reasonable, and that generation of high-quality distractors is more challenging than question and answer generation. More generally, while it may be too early to replace manually generated tests for summative feedback and grading with automatic quiz generation, EduQuiz already has potential value for formative feedback and to increase engagement during the learning phase by enhancing textbooks with assessments.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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