From A rabic Text to Puzzles: LLM -Driven Development of A rabic Educational Crosswords (2025)

Abstract: We present an Arabic crossword puzzle generator from a given text that utilizes advanced language models such as GPT-4-Turbo, GPT-3.5-Turbo, and Llama3-8B-Instruct, specifically developed for educational purposes, this innovative generator leverages a meticulously compiled dataset named Arabic-Clue-Instruct with over 50,000 entries encompassing text, answers, clues, and categories. This dataset is intricately designed to aid in the generation of pertinent clues linked to specific texts and keywords within defined categories. This project addresses the scarcity of advanced educational tools tailored for the Arabic language, promoting enhanced language learning and cognitive development. By providing a culturally and linguistically relevant tool, our objective is to make learning more engaging and effective through gamification and interactivity. Integrating state-of-the-art artificial intelligence with contemporary learning methodologies, this tool can generate crossword puzzles from any given educational text, thereby facilitating an interactive and enjoyable learning experience. This tool not only advances educational paradigms but also sets a new standard in interactive and cognitive learning technologies.

## introduction crossword puzzles, traditionally enjoyed for their challenge and entertainment value, are increasingly recognized for their educational potential. these puzzles facilitate learning in multiple disciplines, such as history, science, and linguistics, and are particularly effective in enhancing vocabulary and spelling skills @xcite @xcite . their capacity to engage and educate simultaneously makes them invaluable in pedagogical contexts. in language acquisition and the mastery of specialized terminology, crossword puzzles stand out as exceptional tools. they offer an interactive learning experience that promotes both the retention of technical jargon and general language skills @xcite @xcite . this dynamic learning approach supports the cognitive development of learners by improving critical thinking abilities and memory retention @xcite @xcite @xcite @xcite . the integration of advanced technologies, such as natural language processing (nlp), further enhances the effectiveness of educational crossword puzzles. the advent of large language models (llms) has particularly revolutionized the creation of arabic educational crosswords, providing sophisticated, context-appropriate clues that significantly enrich the learning experience. this paper introduces a novel application that leverages llms to produce tailored educational crossword puzzles from given text in a chosen category for arabic learners. the application creates high-quality clues and answers, integrating userprovided texts or keywords through fine-tuning techniques. which can be utilized by educators to develop more engaging and effective instructional methodologies for learners. moreover, to support the development and assessment of this tool, a new dataset has been compiled named arabic-clue-instruct, which will be disseminated to the scientific community. this dataset, accessible in an open-source format, consists of categorized texts in arabic, each corresponding with clues and keywords in various learning categories. this initiative aims to streamline the creation of educational crosswords and facilitate their adoption in educational settings. the structure of the paper is as follows. section 2 provides a detailed review of the relevant literature. section 3 contains the data set collection methodology and the curation process. the computational methodologies used are also elucidated. section 4 discusses the results of our experimental evaluations. finally, section 5 offers concluding remarks on the results and implications of our study. and section 6 outlines the limitations of this study. ## related works the field of crossword puzzle generation has attracted significant academic interest due to its complex nature. researchers have experimented with a wide array of approaches including the use of vast lexical databases and embracing the potential of modern large language models (llms) @xcite , which accommodate innovative methods like fine-tuning and zero/fewshot learning. pioneer works in this domain have been diverse and innovative. for example, rigutini and his colleagues. successfully employed advanced natural language processing (nlp) techniques to automatically generate crossword puzzles directly from sources based on the internet, marking a fundamental advancement in this field @xcite @xcite . in addition, ranaivo and his colleagues. devised an approach based on nlp that utilizes text analytics alongside graph theory to refine and pick clues from texts that are topic-specific @xcite . meanwhile, aimed at catering to spanish speakers, esteche and his group developed puzzles utilizing electronic dictionaries and news articles for crafting clues @xcite . on another front, arora and his colleagues. introduced seekh, which merges statistical and linguistic analyzes to create crossword puzzles in various indian languages, focusing on keyword identification to structure the puzzles @xcite . recent advances have included the efforts of zeinalipour and his colleagues, who demonstrated how large-scale language models can be used to develop crossword puzzles in lesser-supported languages, including english, arabic, italian and turkish showcasing the broad applicability of computational linguistics in generating puzzles that are both engaging and linguistically rich @xcite . in (zugarini et al., 2024) they suggest a method for creating educational crossword clues and text datasets in english. in the arabic version of generating crossword puzzles from text @xcite , a few-shot learning approach was utilized, employing large language models (llms) as they are. however, in our current project, we have introduced a dataset specifically designed for this task in arabic. additionally, we have developed open-source models fine-tuned to better accommodate and enhance performance for this specific application. however, the unique characteristics and challenges presented by the arabic language have remained largely unexplored in the context of the generation of crossword puzzles from a given text. this study breaks new ground by explicitly employing cutting-edge language modeling techniques to create arabic crossword puzzles from a given text. this approach not only fills a gap within the scholarly literature, but also enhances the tools available for language-based educational initiatives, thus advancing the field of arabic crossword puzzle generation. ## methodology this paper outlines the development of an innovative tool: an automated system to generate educational crossword puzzles from given text in arabic, driven by large language models (llm). we introduce the arabic-clue-instruct dataset, which comprises a curated selection of arabic texts across several educational fields, including chemistry, biology, geography, philosophy, and history, among others.our research is not confined to the specific category utilized in this study; it can be generalized to encompass a broader range of categories. this generalizability is attributed to the inherent capabilities of llms.this dataset served as the foundation for creating tailored crossword clues for each subject area. an exhaustive iterative procedure enabled us to enrich the dataset, ensuring a diversified representation of arabic educational material and specific crossword clues to correspond with each thematic area. our primary objective was to engineer arabic crossword puzzles directly from textual content, employing the arabic-clue-instruct dataset. we accomplished this by initially extracting the main context and the suitable keywords then generating human-evaluated clues using gpt-4-turbo 3.1 then fine-tuning a suite of llms to adeptly generate accurate and pertinent crossword clues. the models employed in this undertaking include llama3-8b-instruct and gpt3.5-turbo . the subsequent sections of this paper will explore the detailed techniques utilized in the creation of this dataset and the customization of llms. this development is expected to enhance learning experiences and outcomes in arabic education. the comprehensive methods employed in this project are visualized in figure 1 . ## experimental results in this section, we will detail the comprehensive experiments conducted in this study. initially, we begin by describing the training setup utilized for training the llms using the arabic-clue-instruct. this includes training parameters and the computational resources employed. subsequently, we present the performance evaluations of the models, highlighting their effectiveness through automated metrics, with a particular focus on the rouge score. we will break down the results to show how the various configurations of the models compare and identify key areas where performance improvements are observed. following this, we delve into an in-depth analysis of the human evaluations conducted to assess the models' performance. this analysis will include the methodology adopted for human evaluation, criteria for assessment, and qualitative feedback from human reviewers. we will provide insights into aspects such as relevance, coherence, and the overall quality of the generated content, which are often beyond the scope of automated metrics. lastly, to illustrate the practical application of our proposed system, we provide an example of a crossword puzzle generated using the trained models. through this detailed examination, we aim to present a holistic understanding of the experiments and their outcomes, demonstrating the robustness and versatility of the proposed system. ## conclusion in this paper, we present a system for generating crossword clues from arabic text. we created a dataset named arabic-clue-instruct that includes text, keywords, categories, and related crossword clues in arabic, making it the first of its kind in this context. utilizing this dataset, we finetuned two large language models (llms), namely