Multiple choice questions (MCQs) are often used in both employee selection and training, providing objectivity, efficiency, and scalability. However, their creation is resource-intensive, requiring significant expertise and financial investment. This study leverages large language models (LLMs) and prompt engineering techniques to automate the generation and validation of MCQs, particularly within the context of university regulations. Mainly, two novel approaches are proposed in this work: an automatic question generation system for university resolution and an automatic evaluation system to assess the performance of MCQ generation systems. The generation system combines different prompt engineering techniques and a review process to create well formulated questions. The evaluation system uses prompt engineering combined with an advanced LLM model to assess the integrity of the generated question. Experimental results demonstrate the effectiveness of both systems. The findings highlight the transformative potential of LLMs in educational assessment, reducing the burden on human resources and enabling scalable, cost-effective MCQ generation.