  Teachers' growth mindset supportive language (GMSL)--rhetoric emphasizing
that one's skills can be improved over time--has been shown to significantly
reduce disparities in academic achievement and enhance students' learning
outcomes. Although teachers espouse growth mindset principles, most find it
difficult to adopt GMSL in their practice due the lack of effective coaching in
this area. We explore whether large language models (LLMs) can provide
automated, personalized coaching to support teachers' use of GMSL. We establish
an effective coaching tool to reframe unsupportive utterances to GMSL by
developing (i) a parallel dataset containing GMSL-trained teacher reframings of
unsupportive statements with an accompanying annotation guide, (ii) a GMSL
prompt framework to revise teachers' unsupportive language, and (iii) an
evaluation framework grounded in psychological theory for evaluating GMSL with
the help of students and teachers. We conduct a large-scale evaluation
involving 174 teachers and 1,006 students, finding that both teachers and
students perceive GMSL-trained teacher and model reframings as more effective
in fostering a growth mindset and promoting challenge-seeking behavior, among
other benefits. We also find that model-generated reframings outperform those
from the GMSL-trained teachers. These results show promise for harnessing LLMs
to provide automated GMSL feedback for teachers and, more broadly, LLMs'
potentiality for supporting students' learning in the classroom. Our findings
also demonstrate the benefit of large-scale human evaluations when applying
LLMs in educational domains.

Computation and Language