  Readability metrics and standards such as Flesch Kincaid Grade Level (FKGL)
and the Common European Framework of Reference for Languages (CEFR) exist to
guide teachers and educators to properly assess the complexity of educational
materials before administering them for classroom use. In this study, we select
a diverse set of open and closed-source instruction-tuned language models and
investigate their performances in writing story completions and simplifying
narratives--tasks that teachers perform--using standard-guided prompts
controlling text readability. Our extensive findings provide empirical proof of
how globally recognized models like ChatGPT may be considered less effective
and may require more refined prompts for these generative tasks compared to
other open-sourced models such as BLOOMZ and FlanT5--which have shown promising
results.
