# Retrieval-Augmented Generation (RAG) Chatbots for Education: A Survey of Applications
## 4 Discussion and Recommendations  
The practical advantages of RAG make it a capable technological solution for the development of chatbots for education [53]. Although the number of their reported applications keeps growing, so far there has been no survey exploring what has been achieved so far in applying RAG chatbots in the educational domain, like there is for, e.g., medical sciences [9]. The presented work addresses this gap. By a careful analysis of the identified body of the literature, we were able to answer the five key questions, helping us to picture the current state of the field. Regarding RQ1, the number of 47 identified relevant publications indicates that the application of RAG chatbots to the educational domain remains a fresh topic. Interestingly, this number is close to the number of relevant papers on the use of RAG in healthcare established by [PERSON] et al. [87]--37, based on two bibliographic sources (PubMed and Google Scholar), showing a similar speed of RAG adoption in both fields. For a comparison, the survey of educational chatbots in general that was performed by [PERSON] et al. using five bibliographic sources already in 2021 reported 80 relevant papers [11]. The novelty of the area is confirmed by the character of the identified works, with only 13 refereed journal papers but 23 conference proceeding papers (having a much shorter submission-to-publication time span) and 9 preprints (having no publication delay at all). With regard to RQ2, the majority of described chatbots (25) aim to support the students, with only 5 clearly aimed to support the teachers, and the remainder (20) are equally useful for both target user groups (these are the chatbots giving access to some subject-specific databases). The purpose of more than a half of the chatbots developed for students is supporting their learning process, whereas the remainder supports other aspects of their education, in most cases, their acclimatization in the educational institution (7) or their choice of courses (3). To put these numbers in context, roughly similar ratios were reported by [PERSON] et al., where, among the 80 analyzed papers, 36 were dedicated to supporting learning and 15 were providing assistance to students [11]. Regarding RQ3, the largest share of chatbots (11) deals with the theme of the institutional-domain knowledge. Focusing at the areas of education, the one most often supported with RAG chatbots is health sciences (nine instances), with computer science coming second (six instances). The interest in the use of chatbots in these two areas is well-established in the literature (see, e.g., refs. [9; 88]). In response to RQ4, we can clearly point to OpenAI's GPT as the range of models by far most frequently used in constructing RAG chatbots in the educational domain. This dominance is consistent with the findings of other authors, e.g., [PERSON] et al., who investigated various uses of LLMs in computer science education, reported 82 papers directly or indirectly using GPT among the 125 papers that they analyzed [88]. Regarding RQ5, we have identified a plethora of methods and criteria used in the evaluation of the educational RAG chatbots. Although several solutions specifically aimed at this particular area have been proposed, many of them highly relevant, they seem to lack either comprehensiveness in their consideration of the analyzed dimensions [61; 85] or a detailed specification of evaluation measures and guidelines [86]; therefore, they cannot replace the other evaluation methods in use in their entirety. We also cannot indicate some methods as objectively better or worse than others, as in reality they all have some merits, covering different aspects of evaluation and having different purposes. Nonetheless, in an attempt to synthesize our findings on the evaluation methods in use and provide some recommendations for future evaluators of RAG chatbots in educational contexts, we have constructed Table 5, matching the aspect or purpose of evaluation with most adequate methods and measures.