Automatic Generation of Corpus-Based Exercises Using Generative AI

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11210%2F25%3A10507804" target="_blank" >RIV/00216208:11210/25:10507804 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2025.rocling-main.9/" target="_blank" >https://aclanthology.org/2025.rocling-main.9/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Generation of Corpus-Based Exercises Using Generative AI

Popis výsledku v původním jazyce

This study explores the automatic generation of corpus-based language exercises using a generative AI model Corpus Linguist. It focuses on the interaction between the language model and corpus data, detailing a workflow in which collocation and translation patterns are extracted from a tagged corpus and structured prompts are constructed to guide the model in producing sentence-level exercises. The generated exercises reveal both the potential and the current limitations of AI-driven approaches. Challenges include inconsistency in corpus data use, and choosing appropriate translation equivalents. These observations highlight the necessity of careful design and critical evaluation when integrating generative models with corpus-based language materials. By analysing these processes from a computational linguistics perspective, this study contributes to understanding how generative AI can interact with structured linguistic data, informing future applications in automated language resources.

Název v anglickém jazyce

Automatic Generation of Corpus-Based Exercises Using Generative AI

Popis výsledku anglicky

This study explores the automatic generation of corpus-based language exercises using a generative AI model Corpus Linguist. It focuses on the interaction between the language model and corpus data, detailing a workflow in which collocation and translation patterns are extracted from a tagged corpus and structured prompts are constructed to guide the model in producing sentence-level exercises. The generated exercises reveal both the potential and the current limitations of AI-driven approaches. Challenges include inconsistency in corpus data use, and choosing appropriate translation equivalents. These observations highlight the necessity of careful design and critical evaluation when integrating generative models with corpus-based language materials. By analysing these processes from a computational linguistics perspective, this study contributes to understanding how generative AI can interact with structured linguistic data, informing future applications in automated language resources.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

60203 - Linguistics

Projekt

<a href="/cs/project/EH23_025%2F0008691" target="_blank" >EH23_025/0008691: AI zaměřená na člověka pro udržitelnou a adaptabilní společnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)

ISBN

979-8-89176-379-1

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

80-86

Název nakladatele

Association for Computational Linguistics

Místo vydání

National Taiwan University, Taipei City, Taiwan

Místo konání akce

National Taiwan University, Taipei City, Taiwan

Datum konání akce

20. 11. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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