  Large Language Models (LLMs) have an increasing impact on our lives with use
cases such as chatbots, study support, coding support, ideation, writing
assistance, and more. Previous studies have revealed linguistic biases in
pronouns used to describe professions or adjectives used to describe men vs
women. These issues have to some degree been addressed in updated LLM versions,
at least to pass existing tests. However, biases may still be present in the
models, and repeated use of gender stereotypical language may reinforce the
underlying assumptions and are therefore important to examine further. This
paper investigates gender biases in LLMs in relation to educational choices
through an open-ended, true to user-case experimental design and a quantitative
analysis. We investigate the biases in the context of four different cultures,
languages, and educational systems (English/US/UK, Danish/DK, Catalan/ES, and
Hindi/IN) for ages ranging from 10 to 16 years, corresponding to important
educational transition points in the different countries. We find that there
are significant and large differences in the ratio of STEM to non-STEM
suggested education paths provided by chatGPT when using typical girl vs boy
names to prompt lists of suggested things to become. There are generally fewer
STEM suggestions in the Danish, Spanish, and Indian context compared to the
English. We also find subtle differences in the suggested professions, which we
categorise and report.
