Cloze multiple-choice questions (MCQs) are essential for assessing comprehension in educational settings, but manually designing effective distractors is time-consuming. Addressing this, recent research has automated distractor generation, yet such methods often neglect to adjust the difficulty level to the learner’s abilities, resulting in non-personalized assessments. This study introduces the Personalized Cloze Test Generation (PCGL) Framework, utilizing Large Language Models (LLMs) to generate cloze tests tailored to individual proficiency levels. Our PCGL Framework simplifies test creation by generating both question stems and distractors from a single input word and adjusts the difficulty to match the learner’s proficiency. The framework significantly reduces the effort in creating tests and enhances personalized learning by dynamically adjusting to the needs of each learner.