mlsanityData to 7 Oct 2026

Automating Turkish Educational Quiz Generation Using Large Language Models

arXiv 2406.03397 ↗First posted 5 Jun 2024cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 5 Jun 2024.

Builds on

21 of 39 references.

References · 21

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  5. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 851
  6. 2017Crowdsourcing Multiple Choice Science Questionscited by 742
  7. 2022Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehensioncited by 79
  8. 2017Neural Question Generation from Text: A Preliminary Studycited by 78
  9. 2017Question Answering and Question Generation as Dual Taskscited by 37
  10. 2017Machine Comprehension by Text-to-Text Neural Question Generationcited by 32
  11. 2020Automatic Distractor Generation for Multiple Choice Questions in Standard Testscited by 26
  12. 2022Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarizationcited by 26
9 more
  1. 2021Question Generation for Adaptive Educationcited by 22
  2. 2020EQG-RACE: Examination-Type Question Generationcited by 15
  3. 2019Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferringcited by 11
  4. 2022EduQG: A Multi-format Multiple Choice Dataset for the Educational Domaincited by 11
  5. 2018Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Featurescited by 10
  6. 2019Multi-Task Learning with Language Modeling for Question Generationcited by 10
  7. 2021I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selectioncited by 7
  8. 2023MultiQG-TI: Towards Question Generation from Multi-modal Sourcescited by 6
  9. 2019Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Modelcited by 4

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