# Retrieval-Augmented Generation (RAG) Chatbots for Education: A Survey of Applications
## 1 Introduction  
While many education researchers believe that dialogue plays a significant role in learning [1] (p. 1), in practice, many students are deprived of the opportunity to talk to their teachers (e.g., when the latter have to teach too many students to be able to find sufficient time to speak with each of them) or fellow students (e.g., in the case of asynchronous online learning when there is no one else online at the given moment). A revolutionary solution to this issue materialized with the introduction of chatbots based on large language models (LLMs), such as OpenAI's ChatGPT, capable of instantly generating highly realistic and convincing conversational responses [2]. An important advantage is that talking to chatbots is free from the apprehension that the students face in exposing their lack of knowledge or understanding to teachers or peers, fostering a more supportive learning environment, as they introduce neither social distance nor a power relationship [3]. Despite their numerous virtues, LLMs also have some flaws; in particular, it is difficult to update or extend their knowledge and they are prone to hallucinating unintended text [4]. These form a serious problem in the use of LLMs for educational purposes, especially the hallucinations, i.e., generating _content that is nonsensical or unfaithful to the provided source content_[5]. A capable solution addressing these flaws comes in the form of Retrieval-Augmented Generation (RAG) [4]. RAG efficiently enhances the performance of LLMs, not by optimizing their training but by integrating information retrieval techniques [6]. In short, RAG uses the input sequence \(x\) to retrieve text documents \(z\) and use them as additional context when generating the target sequence \(y\)[4]. Although RAG has its limitations, such as a possible failure to retrieve documents most relevant to the query, a possible failure of the generative module to effectively incorporate the retrieved information into its response, and thecomputational overhead because of both a retrieval and a generation step being performed for each query [7], nonetheless, it has established itself as a practical means for developing numerous LLM-based applications serving various purposes [8]. It is especially useful in areas where model reliability is of primary importance, such as healthcare [9]. Although education can also be considered such an area, so far there has been no survey dedicated to unveiling the progress made in applying RAG there. This research gap requires attention for a number of reasons, including the expected continued quick development of the field of RAG applications in the educational domain, which calls for defining the state of the progress attained so far; the revolutionary character of chatbots featuring RAG, making them notably distinct from chatbots developed in the pre-RAG era and covered in the educational chatbot surveys published so far [10; 11; 12]; and the unique character of RAG chatbots developed for the educational domain (in particular, their specific range of targets of support), different from the specific traits of RAG chatbots developed for other domains, such as healthcare where, e.g., patient safety is of primary concern [9]. This paper aims to address this gap by investigating the development of the primary kind of RAG applications, that is, RAG-based chatbots in the educational domain. Based on works published so far, not only in peer-reviewed venues, but also as preprints and theses available online, this survey attempts to answer the following research questions:  
* How many and what kinds of works have been published on the topic so far? * What are the purposes served by the RAG-based chatbots in the educational domain? * What are the themes covered by the RAG-based chatbots in the educational domain? * Which of the available large language models are used by the RAG-based chatbots in the educational domain? * What are the criteria according to which the RAG-based educational chatbots were evaluated? This paper is structured as follows.