Exploring the use of retrieval-augmented generation models in higher education
A pilot study on artificial intelligence-based tutoring
Bibliographic Data
| ID | 22203333 |
|---|---|
| Authors | Renáta Németh (0000-0001-6796-3563, Eötvös Loránd University, corresponding author), Annamária Tátrai (Eötvös Loránd University), Miklós Szabó (0000-0002-5853-2259, Eötvös Loránd University), Péter Tibor Zaletnyik (Obuda University), Árpád Tamási (Eötvös Loránd University) |
| Year | 2025 |
| Volume | 12 |
| Pages | 101751 |
| Publication date | 2025-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Sciences & Humanities Open (JOURNAL) |
| Journal identifiers | ISSN: 2590-2911 • E-ISSN: 2590-2911 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ssaho.2025.101751 |
| OpenAlex | W4412056434 |
| Language | EN |
| Citations received | 1 |
| References cited | 9 |
The role of artificial intelligence in education is growing, but its impact on teaching and learning remains unclear. This paper examines an artificial intelligence tutor like ChatGPT, enhanced with retrieval-augmented generation, in a pilot project at two universities. The advantage of retrieval-augmented generation in higher education is that lecturers can take control of the underlying literature used by the large language model. Four courses from three programs with different learning objectives and pedagogical methods were involved to provide as diverse a context as possible. We created an artificial intelligence tutor for each course, capable of answering students' questions by referring to the educational resources provided. Both students and lecturers reported positive experiences. Qualitative analysis of the chats shows a high level of engagement and motivation. Based on expert coding of a random sample, 1.5 % of answers were incorrect and 16.5 % were outside the context provided to the large language model. We found that retrieval-augmented generation reduces hallucinations for topics that are sufficiently explained in the material. Both the online survey results and the temporal pattern of the queries showed various learning strategies. Students used the tutor effectively, intuitively finding the most appropriate use for themselves. A comparison of different courses shows that the same tool can work differently in different contexts. Our results show that the artificial intelligence tutor does not simply impart knowledge, but mediates the relationship between student, lecturer, and course material. This mediation can facilitate learning, but it can also pose new challenges
Human–computer interaction · Information retrieval · Multimedia · Computer Science · Intelligent Tutoring Systems and Adaptive Learning · Natural Language Processing Techniques · Topic Modeling · Artificial Intelligence
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |