The use of generative AI in education is rapidly evolving, providing new opportunities for personalized learning experiences. Traditional education systems often lack the flexibility to adapt to individual learners’ needs, leading to gaps in effective knowledge dissemination. One key challenge is the ability to dynamically generate educational content tailored to specific learning objectives, topics, and difficulty levels. Current methods for content creation are time-consuming and lack scalability. In this study, a Generative AI-based approach was implemented using GPT-2, a state-of-the-art language model, to automatically generate customized training material based on user inputs such as topic, difficulty level, and learning objectives. The generated content was further analyzed using natural language processing techniques, including word frequency distribution, sentiment analysis, and part-of-speech tagging. The generated content was relevant to the selected topic, with key terms such as “AI,” “clinical,” and “health” dominating the output. Sentiment analysis showed that 88.2% of the content was neutral, 7% positive, and 4.8% negative, indicating that the material maintains an objective tone suitable for educational purposes. Sentence length analysis revealed a mix of simple and complex structures, ensuring the generated material is accessible yet informative. Our findings suggest that generative AI can be effectively applied to create personalized learning content, enhancing the adaptability and scalability of educational and training programs. This approach could be extended to various fields, providing tailored learning experiences for diverse audiences.