A rab I cros: AI -Powered A rabic Crossword Puzzle Generation for Educational Applications (2023)

Abstract: This paper presents the first Arabic crossword puzzle generator driven by advanced AI technology. Leveraging cutting-edge large language models including GPT4, GPT3-Davinci, GPT3-Curie, GPT3-Babbage, GPT3-Ada, and BERT, the system generates distinctive and challenging clues. Based on a dataset comprising over 50,000 clue-answer pairs, the generator employs fine-tuning, few/zero-shot learning strategies, and rigorous quality-checking protocols to enforce the generation of high-quality clue-answer pairs. Importantly, educational crosswords contribute to enhancing memory, expanding vocabulary, and promoting problem-solving skills, thereby augmenting the learning experience through a fun and engaging approach, reshaping the landscape of traditional learning methods. The overall system can be exploited as a powerful educational tool that amalgamates AI and innovative learning techniques, heralding a transformative era for Arabic crossword puzzles and the intersection of technology and education.

## introduction combining traditional puzzle constructs with educational components, pedagogical crosswords foster interactive learning experiences by integrating vocabulary, history, sciences, and other subjects. intriguingly, they effectively strengthen students' vocabulary and spelling abilities due to the puzzles' requirement for accurate spelling @xcite @xcite . these puzzles are particularly significant for language acquisition and learning specific technical terms @xcite @xcite . moreover, they enhance problem-solving, critical thinking skills, and memory retention, thereby making the learning process enjoyable and productive @xcite @xcite @xcite @xcite . creating arabic educational crosswords can be challenging due to the required wordplay expertise. however, with the help of innovations in natural language processing, large language models (llms) are now able to generate high-quality arabic crossword clues. llms are pre-trained on a mix of sources like books, academic articles, and web content and this wide spectrum of content enables them to create challenging and engaging crossword clues. this aids puzzle designers and improves the solver's experience, enabling even beginners to design personalized puzzles. the results show that the proposed approach can be effectively employed to generate arabic educational crossword puzzles, introducing an innovative system using llms to generate top-quality clues and answers. by inputting text passages or keywords, the system generates clue-answer pairs, based on techniques like fine-tuning and few-shot learning used for generation. we also present models to filter inappropriate clue-answer pairs for puzzle construction optimization, propose an advanced algorithm for designing arabic educational crossword layouts, and provide a comprehensive dataset of curated arabic clue-answer pairs. these advances simplify the creation of arabic pedagogical crosswords and expand their potential for their broader exploitation. this paper is structured as follows; section 2 explores relevant literature; section 3 discusses the collected arabic dataset; section 4 outlines the research methodology, section 5 presents the findings, and, finally, section 6 summarizes the overall outcomes. ## related works the generation of crosswords represents a complex task that has been addressed by some research works. these studies have utilized a variety of tools, including traditional dictionaries and thesauri, or have engaged in the linguistic analysis of text content derived from the web. rigutini et al. @xcite pioneered the first fully automated crossword creator system in 2008. the proposed system leverages natural language processing techniques to generate crossword clues by scraping related documents from the web, extracting relevant text segments, and using part-of-speech tagging, dependency parsing, and wordnet-based similarity measures. this approach produces clues based on specific ranking criteria. an alternative methodology for crossword construction using natural language processing is documented in @xcite . this approach consists of a four-stage process, which includes initial data retrieval of a targeted topicspecific text compilation, extraction of complete sentences, determination of the dependency syntactic structure of each sentence, and removal of words from stop-lists. the extracted information undergoes a transformation into a graph representation for depth-first pre-order search. this framework integrates pre-processing, candidate identification, clue formation, and answer selection. esteche et al.'s study @xcite delved into the creation of spanish language crossword puzzles from news articles. the system is based on a twofold procedure: initially pivotal terms are identified and their meanings are isolated from a trusted online dictionary. subsequently, these definitions are employed as hints for the assembly of compelling crossword puzzles. in a related study, arora et al. @xcite discuss a software tool that uses nlp techniques to identify crucial keywords for creating crossword puzzles in various indian languages. their proposed framework, seekh, combines statistical and linguistic methods to highlight significant keywords useful for crossword creation. despite significant research, accurately generating comprehensive and unique clue-answer sets from linguistic corpora remains a challenge, particularly for the unique linguistic nuances of arabic. to address these issues, we propose an innovative methodology using llms to create intricate educational clues. as a pioneering attempt, our technique successfully generates arabic crossword puzzles, filling a gap unaddressed by previous methods. by generating intellectually stimulating and original crossword puzzles, this novel approach enhances learners' deep understanding of the subjects by providing comprehensive answers. hence, the proposed work not only brings novelty to arabic crossword generation, but also offers a groundbreaking solution in the realm of educational tools. ## dataset given the scarcity of data for arabic crossword puzzles, a clue-answer pair dataset was gathered manually. the dataset encompasses the period from 2020 to 2023. during the initial stage of data collection, we pursued all accessible crossword puzzles, encompassing web-based games, journals, and magazines, ensuring that the training set comprised accurate clueanswer pairs sourced from original arabic crossword puzzles. we had a collection of crossword images, and we needed to extract the text contained within these images to build a dataset for obtaining the text from these images. to accomplish this, we initially utilized optical character recognition (ocr) as a tool. however, it's important to note that the ocr process was predominantly supervised by humans who used it to facilitate the extraction. additionally, human validation was employed to evaluate both spelling errors within the journals and the overall quality of the clue-answer pairs. this meticulous process resulted in a catalog of 57,706 entries from two different sources. one of them was the al-joumhouria journal, from which we manually extracted 5,661 clue and answer pairs. the other source was the al-ghad electronic journal, where we utilized the ocr tool to assist in the extraction process. in the end, this yielded 25,908 unique pairs with answers varying in length from 1 to 21 characters, with the majority of the data falling within a specific answer's character length range from 2 to 9 (see fig. 1 ). the structure of the pairs is recurrent. for instance, some of the pairs are synonyms or antonym definitions, that define the answer by means of one or more synonyms or antonyms. an example of this category includes " the proposed system includes several components, such as mechanisms to generate clue-answer pairs using user-provided text or keywords, and a crossword schema generator as depicted in figure 2 . users can input any instructional text to extract relevant clue-answer pairs or insert a list of chosen keywords to generate clues. after combining both clue-generation methods, the quality of the generated pairs is evaluated using specific validation modules. users can then review and select their preferred clue-answer sets, which are employed in the final step by a separate module for creating the crossword layout. ## experiments in this section, we detail the empirical evaluation of the proposed system, focusing on individual elements and their roles within the overall framework.