Unified Modeling Language (UML) diagrams are essential tools for visualizing system structure and behavior in software design. With the rise of using Large Language Models (LLMs) in automating various phases of software development, there is growing interest in automating UML diagrams generation. To that end, this study presents an empirical investigation into the effectiveness of LLMs, GPT-4-turbo, in generating structural (Class, Deployment) and behavioral (Use Case, Sequence) UML diagrams. A rule-based prompt engineering was developed to transform domain scenarios, extracted from a widely used UML textbook, into optimized prompts fed to LLMs. Then, UML diagrams were automatically synthesized using PlantUML, and evaluated through a survey of 121 computer science and software engineering students across three U.S. universities. Participants assessed completeness and correctness for both of LLM-assisted and Human-created diagrams by checking various elements for each single UML diagram. Statistical analyses, including paired t-tests, Wilcoxon signed-rank tests, and Pearson correlation, were conducted to validate the results. Findings revealed that LLM-assisted diagrams achieve completeness and correctness scores of 65%, 61.1% for Class diagram, 65.9%, 64.3% for Deployment diagram, 67.1%, 64.2% for Use Case diagram, and 67.7%, 66.2% for Sequence diagram. Whereas, the completeness and correctness for human-created diagrams reported as (79.8%, 76.3%), (70%, 73%), (80.7%, 80.4%), and (73.2%, 72.6%) for Class, Deployment, Use Case, and Sequence diagrams accordingly. Obviously, Class and Use Case diagrams show less similarity comparing to human-created models, while Deployment and Sequence diagrams show stronger alignment.