Transformer architectures contribute to managing long-term dependencies for
Natural Language Processing, representing one of the most recent changes in the
field. These architectures are the basis of the innovative, cutting-edge Large
Language Models (LLMs) that have produced a huge buzz in several fields and
industrial sectors, among the ones education stands out. Accordingly, these
generative Artificial Intelligence-based solutions have directed the change in
techniques and the evolution in educational methods and contents, along with
network infrastructure, towards high-quality learning. Given the popularity of
LLMs, this review seeks to provide a comprehensive overview of those solutions
designed specifically to generate and evaluate educational materials and which
involve students and teachers in their design or experimental plan. To the best
of our knowledge, this is the first review of educational applications (e.g.,
student assessment) of LLMs. As expected, the most common role of these systems
is as virtual tutors for automatic question generation. Moreover, the most
popular models are GTP-3 and BERT. However, due to the continuous launch of new
generative models, new works are expected to be published shortly.

利用 Transformer 架构的大型语言模型在教育领域引起了广泛关注，主要应用包括生成和评估教育材料、自动问题生成等，其中 GTP-3 和 BERT 是最受欢迎的模型。