aifeducation - Artificial Intelligence for Education
In social and educational settings, the use of Artificial
Intelligence (AI) is a challenging task. Relevant data is often
only available in handwritten forms, or the use of data is
restricted by privacy policies. This often leads to small data
sets. Furthermore, in the educational and social sciences, data
is often unbalanced in terms of frequencies. To support
educators as well as educational and social researchers in
using the potentials of AI for their work, this package
provides a unified interface for neural nets in 'PyTorch' to
deal with natural language problems. In addition, the package
ships with a shiny app, providing a graphical user interface.
This allows the usage of AI for people without skills in
writing python/R scripts. The tools integrate existing
mathematical and statistical methods for dealing with small
data sets via pseudo-labeling (e.g. Cascante-Bonilla et al.
(2020) <doi:10.48550/arXiv.2001.06001>) and imbalanced data via
the creation of synthetic cases (e.g. Bunkhumpornpat et al.
(2012) <doi:10.1007/s10489-011-0287-y>). Performance
evaluation of AI is connected to measures from content analysis
which educational and social researchers are generally more
familiar with (e.g. Berding & Pargmann (2022)
<doi:10.30819/5581>, Gwet (2014) <ISBN:978-0-9708062-8-4>,
Krippendorff (2019) <doi:10.4135/9781071878781>). Estimation of
energy consumption and CO2 emissions during model training is
done with the 'python' library 'codecarbon'. Finally, all
objects created with this package allow to share trained AI
models with other people.
