Evaluating LLM-Generated Q&A Test: a Student-Centered Study
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908480629112832 |
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| author | Wróblewska, Anna Grabek, Bartosz Świstak, Jakub Dan, Daniel |
| author_facet | Wróblewska, Anna Grabek, Bartosz Świstak, Jakub Dan, Daniel |
| contents | This research prepares an automatic pipeline for generating reliable question-answer (Q&A) tests using AI chatbots. We automatically generated a GPT-4o-mini-based Q&A test for a Natural Language Processing course and evaluated its psychometric and perceived-quality metrics with students and experts. A mixed-format IRT analysis showed that the generated items exhibit strong discrimination and appropriate difficulty, while student and expert star ratings reflect high overall quality. A uniform DIF check identified two items for review. These findings demonstrate that LLM-generated assessments can match human-authored tests in psychometric performance and user satisfaction, illustrating a scalable approach to AI-assisted assessment development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06591 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating LLM-Generated Q&A Test: a Student-Centered Study Wróblewska, Anna Grabek, Bartosz Świstak, Jakub Dan, Daniel Computation and Language Human-Computer Interaction This research prepares an automatic pipeline for generating reliable question-answer (Q&A) tests using AI chatbots. We automatically generated a GPT-4o-mini-based Q&A test for a Natural Language Processing course and evaluated its psychometric and perceived-quality metrics with students and experts. A mixed-format IRT analysis showed that the generated items exhibit strong discrimination and appropriate difficulty, while student and expert star ratings reflect high overall quality. A uniform DIF check identified two items for review. These findings demonstrate that LLM-generated assessments can match human-authored tests in psychometric performance and user satisfaction, illustrating a scalable approach to AI-assisted assessment development. |
| title | Evaluating LLM-Generated Q&A Test: a Student-Centered Study |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.06591 |