# Automating Turkish Educational Quiz Generation Using Large Language Models

arXiv: https://arxiv.org/abs/2406.03397 · HTML: https://mlsanity.com/paper/2406.03397/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [Crowdsourcing Multiple Choice Science Questions](https://mlsanity.com/paper/1707.06209/) (2017)
- [Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension](https://mlsanity.com/paper/2203.13947/) (2022)
- [Neural Question Generation from Text: A Preliminary Study](https://mlsanity.com/paper/1704.01792/) (2017)
- [Question Answering and Question Generation as Dual Tasks](https://mlsanity.com/paper/1706.02027/) (2017)
- [Machine Comprehension by Text-to-Text Neural Question Generation](https://mlsanity.com/paper/1705.02012/) (2017)
- [Automatic Distractor Generation for Multiple Choice Questions in Standard Tests](https://mlsanity.com/paper/2011.13100/) (2020)
- [Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization](https://mlsanity.com/paper/2203.14187/) (2022)
- [Question Generation for Adaptive Education](https://mlsanity.com/paper/2106.04262/) (2021)
- [EQG-RACE: Examination-Type Question Generation](https://mlsanity.com/paper/2012.06106/) (2020)
- [Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring](https://mlsanity.com/paper/1912.00879/) (2019)
- [EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain](https://mlsanity.com/paper/2210.06104/) (2022)
- [Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features](https://mlsanity.com/paper/1809.02637/) (2018)
- [Multi-Task Learning with Language Modeling for Question Generation](https://mlsanity.com/paper/1908.11813/) (2019)
- [I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection](https://mlsanity.com/paper/2110.04123/) (2021)
- [MultiQG-TI: Towards Question Generation from Multi-modal Sources](https://mlsanity.com/paper/2307.04643/) (2023)
- [Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model](https://mlsanity.com/paper/1910.08832/) (2019)

## Built on by (most cited first, 1 of 1)

- [There Are No Silly Questions: Evaluation of Offline LLM Capabilities from a Turkish Perspective](https://mlsanity.com/paper/2603.09996/) (2026)

## 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.