A H AzniFarida Hazwani Mohd RidzuanNajwa Hayaati Mohd AlwiSakinah Ali PitchayZainur Rijal Abd RazakHanif Ridzwan Ahmad RodziAhmad A AlSabhany2026-04-142026-04-142026A H Azni, Farida Ridzuan, Najwa Hayaati Mohd Alwi , Sakinah Ali Pitchay, Zainur Rijal Abd Razak, Hanif Ridzwan Ahmad Rodzi & Ahmad A AlSabhany (2026) Advanced NLP Techniques for Generating Contextual and Grammatical Arabic Exam Questions. Malaysian Journal of Science Health & Technology, 11(3), 44-53. https://doi.org/10.33102/mjosht.5252601-000310.33102/mjosht.525https://mjosht.usim.edu.my/index.php/mjosht/article/view/525/286https://oarep.usim.edu.my/handle/123456789/29473Indexed by MyCiteThis paper outlines the development of an Arabic exam question generator that utilizes advanced Natural Language Processing (NLP) techniques and a comprehensive Arabic corpus. The primary aim is to aid educators in automating the process of crafting exam questions tailored specifically for A1-level Arabic learners. By harnessing the capabilities of NLP, the system integrates sequence-to-sequence (seq2seq) models and template-based methods to generate educationally appropriate questions. The seq2seq models are designed to predict the next word in a sequence, ensuring that the generated questions are natural and contextually fitting. This approach enables the system to produce logically coherent questions that align with the given context. Moreover, the template-based method guarantees grammatical accuracy, which is essential for educational purposes. The templates use as structured guidelines that steer the seq2seq models, ensuring that the questions adhere to proper grammatical rules and structures. A vital aspect of the system is the incorporation of the AraBERT pre-trained model. AraBERT, a transformer-based model customized for Arabic, undergoes fine-tuning with a specifically annotated dataset to adapt it to the task of generating questions from simple Arabic sentences, thereby enhancing its ability to handle the intricacies of the Arabic language. By combining seq2seq models for contextual relevance and template-based methods for grammatical precision, this dual approach effectively addresses the unique challenges associated with Arabic NLP. The richness of Arabic morphology and its syntactic complexity pose significant hurdles for NLP applications. Through the integration of these methodologies, the system ensures that the generated questions are not only contextually relevant but also grammatically correct, making it a valuable tool for educators. In conclusion, the paper discusses an innovative application of advanced NLP techniques and Arabic corpus utilization, providing a robust solution for automated Arabic exam question generation. This system holds significant potential for enhancing the efficiency and effectiveness of language instruction for Arabic learners.en-USNLPExam Question GenerationArabic Corpustext::journal::journal article445311Special Issue