  Large language models (LLMs) offer a range of new possibilities, including
adapting the text to different audiences and their reading needs. But how well
do they adapt? We evaluate the readability of answers generated by four
state-of-the-art LLMs (commercial and open-source) to science questions when
prompted to target different age groups and education levels. To assess the
adaptability of LLMs to diverse audiences, we compare the readability scores of
the generated responses against the recommended comprehension level of each age
and education group. We find large variations in the readability of the answers
by different LLMs. Our results suggest LLM answers need to be better adapted to
the intended audience demographics to be more comprehensible. They underline
the importance of enhancing the adaptability of LLMs in education settings to
cater to diverse age and education levels. Overall, current LLMs have set
readability ranges and do not adapt well to different audiences, even when
prompted. That limits their potential for educational purposes.
