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        <title>Automatic Generation of Corpus-Based Exercises Using Generative AI</title>
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    <abstract>This study explores the automatic generation of corpus-based language exercises using generative AI models. We focus on the interaction between language models and corpus data, detailing a workflow in which lexical and syntactic 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 of AI-driven approaches. However, 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.</abstract>
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%0 Conference Proceedings
%T Automatic Generation of Corpus-Based Exercises Using Generative AI
%A Zasina, Adrian Jan
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F zasina-2025-automatic
%X This study explores the automatic generation of corpus-based language exercises using generative AI models. We focus on the interaction between language models and corpus data, detailing a workflow in which lexical and syntactic 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 of AI-driven approaches. However, 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.
%U https://aclanthology.org/2025.rocling-main.9/
%P 80-86

Markdown (Informal)

[Automatic Generation of Corpus-Based Exercises Using Generative AI](https://aclanthology.org/2025.rocling-main.9/) (Zasina, ROCLING 2025)

• Automatic Generation of Corpus-Based Exercises Using Generative AI (Zasina, ROCLING 2025)

ACL

• Adrian Jan Zasina. 2025. Automatic Generation of Corpus-Based Exercises Using Generative AI. In Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025), pages 80–86, National Taiwan University, Taipei City, Taiwan. Association for Computational Linguistics.