Introduction

Several packages allow users to use machine learning directly in R such as nnet for single layer neural nets, rpart for decision trees, and ranger for random forests. Furthermore, with mlr3verse a series of packages exists for managing different algorithms with a unified interface.

These packages can be used with a ‘normal’ computer and provide an easy installation. In terms of natural language processing, these approaches are currently limited. State-of-the-art approaches rely on neural nets with multiple layers and consist of a huge number of parameters making them computationally demanding. With specialized libraries such as keras, PyTorch and tensorflow, graphical processing units (gpu) can help to speed up computations significantly. However, many of these specialized libraries for machine learning are written in python. Fortunately, an interface to python is provided via the R package reticulate.

The R package Artificial Intelligence for Education (aifeducation) aims to provide educators, educational researchers, and social researchers with a convincing interface to these state-of-the-art models for natural language processing and tries to address the special needs and challenges of the educational and social sciences. The package currently supports the application of Artificial Intelligence (AI) for tasks such as text embedding and classification.

Since state-of-the-art approaches in natural language processing rely on large models compared to classical statistical methods (e.g., latent class analysis, structural equation modeling) and are based largely on python, some additional installation steps are necessary.

If you would like to train and to develop your own models and AIs, a compatible graphic device is necessary. Even a low performing graphic device can speed up computations significantly. If you prefer to use pre-trained models however, this is not necessary. In this case, a ‘normal’ office computer without a graphic device should be sufficient in most cases. To get ready for using the package, two steps are necessary.

Step 1 - Install the R package

First you need to install the package. This can be done by:

install.packages("aifeducation")

With this command, aifeducation is installed on your machine.

Step 2 - Install Python and optional R packages

Since natural language processing with neural nets is based on models which are computationally intensive, PyTorch is used within this package together with some other specialized python libraries.

The most straightforward method for getting started is to call the function install_aifeducation as follows:

install_aifeducation(
  install_aifeducation_studio = TRUE
)

This function will install python, miniconda, and all relevant python libraries into a conda environment called “aifeducation”.

In addition, we recommend to set install_aifeducation_studio=TRUE since this will install the optional R packages necessary to use AI for Education - Studio. AI for Education - Studio is a graphical user interface for applying these packages. We recommend to use it for everyone who is unfamiliar with R or machine learning. If you do not want use the studio, you can set the argument to FALSE. In this case you have to use the package with R syntax.

If you have a suitable machine and would like to use a graphic card for computations you need to install some further software. You can find further information here: https://pytorch.org/get-started/locally/

You can check if python is working by using the function reticulate::py_available(). This should return TRUE.

reticulate::py_available(initialize = TRUE)

You can check if all necessary python packages are successfully installed by calling the function check_aif_py_modules

aifeducation::check_aif_py_modules()

Now everything is ready to use the package.