Type: Package
Package: aifeducation
Title: Artificial Intelligence for Education
Version: 1.1.5
Authors@R: c(
    person("Berding", "Florian", , "florian.berding@uni-hamburg.de", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-3593-1695")),
    person("Tykhonova", "Yuliia", , "yuliia.tykhonova@uni-hamburg.de", role = "aut",
           comment = c(ORCID = "0009-0006-9015-1006")),
    person("Pargmann", "Julia", , "julia.pargmann@uni-hamburg.de", role = "ctb",
           comment = c(ORCID = "0000-0003-3616-0172")),
    person("Leube", "Anna", , "anna.leube@uni-hamburg.de", role = "ctb",
           comment = c(ORCID = "0009-0001-6949-1608")),
    person("Riebenbauer", "Elisabeth", , "elisabeth.riebenbauer@uni-graz.at", role = "ctb",
           comment = c(ORCID = "0000-0002-8535-3694")),
    person("Rebmann", "Karin", , "karin.rebmann@uni-oldenburg.de", role = "ctb"),
    person("Slopinski", "Andreas", , "andreas.slopinski@uni-oldenburg.de", role = "ctb")
  )
Description: 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.
License: GPL-3
URL: https://fberding.github.io/aifeducation/
BugReports: https://github.com/FBerding/aifeducation/issues
Depends: R (>= 3.5.0)
Imports: doParallel, foreach, iotarelr(>= 0.1.5), methods, Rcpp (>=
        1.0.10), reshape2, reticulate (>= 1.42.0), rlang, stringi,
        utils
Suggests: bslib, DT, fs, future, ggplot2, knitr, pkgdown, promises,
        readtext, readxl, rmarkdown, shiny(>= 1.9.0), shinyFiles,
        shinyWidgets, shinycssloaders, sortable, testthat (>= 3.0.0)
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, r6 = TRUE)
RoxygenNote: 7.3.3
SystemRequirements: PyTorch (see vignette "Get started")
Config/Needs/website: rmarkdown
Config/pak/sysreqs: libicu-dev libpng-dev python3
Repository: https://fberding.r-universe.dev
Date/Publication: 2026-04-26 14:05:56 UTC
RemoteUrl: https://github.com/fberding/aifeducation
RemoteRef: HEAD
RemoteSha: e5d96bf0960d0bd0c1349c7356b8129d348dee07
NeedsCompilation: yes
Packaged: 2026-07-04 08:07:30 UTC; root
Author: Berding Florian [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-3593-1695>),
  Tykhonova Yuliia [aut] (ORCID: <https://orcid.org/0009-0006-9015-1006>),
  Pargmann Julia [ctb] (ORCID: <https://orcid.org/0000-0003-3616-0172>),
  Leube Anna [ctb] (ORCID: <https://orcid.org/0009-0001-6949-1608>),
  Riebenbauer Elisabeth [ctb] (ORCID:
    <https://orcid.org/0000-0002-8535-3694>),
  Rebmann Karin [ctb],
  Slopinski Andreas [ctb]
Maintainer: Berding Florian <florian.berding@uni-hamburg.de>