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. Islam et al. (2012)
<doi:10.1016/j.asoc.2021.108288>). 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.
