  This study introduces the leveled-text generation task, aiming to rewrite
educational materials to specific readability levels while preserving meaning.
We assess the capability of GPT-3.5, LLaMA-2 70B, and Mixtral 8x7B, to generate
content at various readability levels through zero-shot and few-shot prompting.
Evaluating 100 processed educational materials reveals that few-shot prompting
significantly improves performance in readability manipulation and information
preservation. LLaMA-2 70B performs better in achieving the desired difficulty
range, while GPT-3.5 maintains original meaning. However, manual inspection
highlights concerns such as misinformation introduction and inconsistent edit
distribution. These findings emphasize the need for further research to ensure
the quality of generated educational content.
