@inproceedings{palomino-etal-2025-edtec,
title = "{E}d{T}ec-{I}tem{G}en: Enhancing Retrieval-Augmented Item Generation Through Key Point Extraction",
author = {Palomino, Alonso and
Buschh{\"u}ter, David and
Roller, Roland and
Pinkwart, Niels and
Paassen, Benjamin},
editor = "Zhang, Yuji and
Chen, Canyu and
Li, Sha and
Geva, Mor and
Han, Chi and
Wang, Xiaozhi and
Feng, Shangbin and
Gao, Silin and
Augenstein, Isabelle and
Bansal, Mohit and
Li, Manling and
Ji, Heng",
booktitle = "Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.knowllm-1.2/",
doi = "10.18653/v1/2025.knowllm-1.2",
pages = "14--25",
ISBN = "979-8-89176-283-1",
abstract = "A major bottleneck in exam construction involves designing test items (i.e., questions) that accurately reflect key content from domain-aligned curricular materials. For instance, during formative assessments in vocational education and training (VET), exam designers must generate updated test items that assess student learning progress while covering the full breadth of topics in the curriculum. Large language models (LLMs) can partially support this process, but effective use requires careful prompting and task-specific understanding. We propose a new key point extraction method for retrieval-augmented item generation that enhances the process of generating test items with LLMs. We exhaustively evaluated our method using a TREC-RAG approach, finding that prompting LLMs with key content rather than directly using full curricular text passages significantly improves item quality regarding key information coverage by 8{\%}. To demonstrate these findings, we release EdTec-ItemGen, a retrieval-augmented item generation demo tool to support item generation in education."
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<abstract>A major bottleneck in exam construction involves designing test items (i.e., questions) that accurately reflect key content from domain-aligned curricular materials. For instance, during formative assessments in vocational education and training (VET), exam designers must generate updated test items that assess student learning progress while covering the full breadth of topics in the curriculum. Large language models (LLMs) can partially support this process, but effective use requires careful prompting and task-specific understanding. We propose a new key point extraction method for retrieval-augmented item generation that enhances the process of generating test items with LLMs. We exhaustively evaluated our method using a TREC-RAG approach, finding that prompting LLMs with key content rather than directly using full curricular text passages significantly improves item quality regarding key information coverage by 8%. To demonstrate these findings, we release EdTec-ItemGen, a retrieval-augmented item generation demo tool to support item generation in education.</abstract>
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%0 Conference Proceedings
%T EdTec-ItemGen: Enhancing Retrieval-Augmented Item Generation Through Key Point Extraction
%A Palomino, Alonso
%A Buschhüter, David
%A Roller, Roland
%A Pinkwart, Niels
%A Paassen, Benjamin
%Y Zhang, Yuji
%Y Chen, Canyu
%Y Li, Sha
%Y Geva, Mor
%Y Han, Chi
%Y Wang, Xiaozhi
%Y Feng, Shangbin
%Y Gao, Silin
%Y Augenstein, Isabelle
%Y Bansal, Mohit
%Y Li, Manling
%Y Ji, Heng
%S Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-283-1
%F palomino-etal-2025-edtec
%X A major bottleneck in exam construction involves designing test items (i.e., questions) that accurately reflect key content from domain-aligned curricular materials. For instance, during formative assessments in vocational education and training (VET), exam designers must generate updated test items that assess student learning progress while covering the full breadth of topics in the curriculum. Large language models (LLMs) can partially support this process, but effective use requires careful prompting and task-specific understanding. We propose a new key point extraction method for retrieval-augmented item generation that enhances the process of generating test items with LLMs. We exhaustively evaluated our method using a TREC-RAG approach, finding that prompting LLMs with key content rather than directly using full curricular text passages significantly improves item quality regarding key information coverage by 8%. To demonstrate these findings, we release EdTec-ItemGen, a retrieval-augmented item generation demo tool to support item generation in education.
%R 10.18653/v1/2025.knowllm-1.2
%U https://aclanthology.org/2025.knowllm-1.2/
%U https://doi.org/10.18653/v1/2025.knowllm-1.2
%P 14-25
Markdown (Informal)
[EdTec-ItemGen: Enhancing Retrieval-Augmented Item Generation Through Key Point Extraction](https://aclanthology.org/2025.knowllm-1.2/) (Palomino et al., KnowLLM 2025)
ACL