In this work, we propose a knowledge-aware approach for Arabic automatic question generation (QG) that leverages the multilingual T5 (mT5) transformer augmented with a pre-trained Arabic question-answering model to address challenges posed by Arabic’s morphological richness and limited QG resources. Our system generates both subjective questions and multiple-choice questions (MCQs) with contextually relevant distractors through a dual-model pipeline that tailors the decoding strategy to each subtask: the question generator employs beam search to maximize semantic fidelity and lexical precision, while the distractor generator uses top-k sampling to enhance diversity and contextual plausibility. The QG model is fine-tuned on Arabic SQuAD, and the distractor model is trained on a curated combination of ARCD and Qudrat. Experimental results show that beam search significantly outperforms top-k sampling for fact-based question generation, achieving a BLEU-4 score of 27.49 and a METEOR score of 25.18, surpassing fine-tuned AraT5 and translated English–Arabic baselines. In contrast, top-k sampling is more effective for distractor generation, yielding higher BLEU scores and producing distractors that are more diverse yet remain pedagogically valid, with a BLEU-1 score of 20.28 establishing a strong baseline in the absence of prior Arabic benchmarks. Human evaluation further confirms the quality of the generated questions. This work advances Arabic QG by providing a scalable, knowledge-aware solution with applications in educational technology, while demonstrating the critical role of task-specific decoding strategies and setting a foundation for future research in automated assessment.
Jabr et al. (Sun,) studied this question.