Automating Turkish Educational Quiz Generation Using Large Language Models
<|reference_start|>Automating Turkish Educational Quiz Generation Using Large Language Models: Crafting quizzes from educational content is a pivotal activity that benefits both teachers and students by reinforcing learning and evaluating understanding. In this study, we introduce a novel approach to generate quizzes from Turkish educational texts, marking a pioneering endeavor in educational technology specifically tailored to the Turkish educational context. We present a specialized dataset, named the Turkish-Quiz-Instruct, comprising an extensive collection of Turkish educational texts accompanied by multiple-choice and short-answer quizzes. This research leverages the capabilities of Large Language Models (LLMs), including GPT-4-Turbo, GPT-3.5-Turbo, Llama-2-7b-chat-hf, and Llama-2-13b-chat-hf, to automatically generate quiz questions and answers from the Turkish educational content. Our work delineates the methodology for employing these LLMs in the context of Turkish educational material, thereby opening new avenues for automated Turkish quiz generation. The study not only demonstrates the efficacy of using such models for generating coherent and relevant quiz content but also sets a precedent for future research in the domain of automated educational content creation for languages other than English. The Turkish-Quiz-Instruct dataset is introduced as a valuable resource for researchers and practitioners aiming to explore the boundaries of educational technology and language-specific applications of LLMs in Turkish. By addressing the challenges of quiz generation in a non-English context specifically Turkish, this study contributes significantly to the field of Turkish educational technology, providing insights into the potential of leveraging LLMs for educational purposes across diverse linguistic landscapes.<|reference_end|>
@article{zeinalipour2024automating,
    title={Automating Turkish Educational Quiz Generation Using Large Language
  Models},
    author={Kamyar Zeinalipour, Yusuf G"okberk Keptiu{g}, Marco Maggini, Marco
  Gori},
    journal={arXiv preprint arXiv:2406.03397},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03397},
    primaryClass={cs.CL}
}