We present PersianMCQ-Instruct, a comprehensive resource that includes a dataset and advanced models for generating multiple-choice questions (MCQs) in standard Iranian Persian, a low-resource language spoken by over 80 million people. This resource features three state-of-the-art models for Persian MCQ generation: PMCQ-Gemma2-9b, PMCQ-Llama3.1-8b, and PMCQ-Mistral-7B. Inspired by the Agent Instruct framework and GPT-4o, we created the dataset by curating over 4,000 unique Persian Wikipedia pages, resulting in three MCQs per page and a total of over 12,000 questions. To ensure the quality of this dataset, we conducted human evaluations and model fine-tuning, both of which demonstrated significant performance improvements in Persian MCQ generation. The dataset and models are publicly available, offering valuable tools for researchers and educators, with particular benefits for advancing Persian-language educational technology.