A Turkish Educational Crossword Puzzle Generator
Project Overview
The document presents a Turkish Educational Crossword Puzzle Generator that harnesses the capabilities of large language models (LLMs) to develop educational crossword puzzles aimed at improving vocabulary, memory, and problem-solving skills among students, particularly in the context of Turkish language education. By leveraging two specially created datasets, the generator produces high-quality clues and solutions tailored to user-suggested texts or keywords, showcasing a notable advancement in the integration of generative AI in educational settings. This innovative tool not only enhances the learning experience but also exemplifies the potential of AI to create personalized and engaging educational content, thereby contributing to effective language acquisition and cognitive skill development in learners.
Key Applications
Turkish Educational Crossword Puzzle Generator leveraging LLMs
Context: Educational context focusing on Turkish language learning, targeting educators and students.
Implementation: The generator uses datasets of answer-clue pairs and text to create crossword puzzles through fine-tuned LLMs like GPT3.5-Turbo and Llama-2.
Outcomes: Enhances student engagement and learning retention; provides an interactive tool for vocabulary development and knowledge acquisition.
Challenges: Initial models struggled with Turkish; the need for high-quality clues that are contextually relevant.
Implementation Barriers
Technical Barrier
Initial language models had limited capability in processing the Turkish language.
Proposed Solutions: Fine-tuning models on specific datasets to improve contextual relevance and quality of generated clues.
Data Availability
Creating high-quality datasets for crossword generation in Turkish was a challenge.
Proposed Solutions: Developing two comprehensive datasets: one for answer-clue pairs and another for categorized texts.
Project Team
Kamyar Zeinalipour
Researcher
Yusuf Gökberk Keptiğ
Researcher
Marco Maggini
Researcher
Leonardo Rigutini
Researcher
Marco Gori
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Kamyar Zeinalipour, Yusuf Gökberk Keptiğ, Marco Maggini, Leonardo Rigutini, Marco Gori
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai