Generative Artificial Intelligence in Education: Conceptual Dimensions, Trends, and Research Directions
Özet
Research into generative artificial intelligence (GenAI) in the field of education has been growing rapidly in recent years. The rapid expansion of the literature has necessitated a systematic examination of the field. In this study, the thematic structure of GenAI research in the field of education during the 2023–2025 period was examined. Given the need to work with a large-scale dataset comprising 15,036 studies, a Latent Dirichlet Allocation (LDA)-based topic modelling analysis was selected. A search conducted on Scopus resulted in the inclusion of 15,036 studies in the analysis. The LDA analysis identified 12 prominent topics based on consistency scores. Descriptively, these prominent topics were grouped under four broad thematic areas. Among these axes, the technical axis holds the largest share (25%). It was observed that whilst technical topics were on the rise during the 2023–2025 period, topics such as ethics and chatbots showed a relative downward trend. The observed trends suggest that scholarly attention is increasingly directed towards technically oriented research. The findings also indicate a need for theoretical frameworks to ensure pedagogical depth. It is anticipated that the study’s findings will serve as an important reference point for future research. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Anahtar Kelimeler
- Scopus
- 0
- WoS
- 0
- OpenAlex
- 0
Tam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| dc.contributor.author | Akti Aslan, Seda | |
| dc.contributor.author | Ayaz, Ahmet | |
| dc.contributor.author | Ozyurt, Ozcan | |
| dc.contributor.author | Aslan, Alper | |
| dc.date.accessioned | 2026-10-28T14:53:55.610867848Z | |
| dc.date.available | 2026-10-28T14:53:55.610867848Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | Research into generative artificial intelligence (GenAI) in the field of education has been growing rapidly in recent years. The rapid expansion of the literature has necessitated a systematic examination of the field. In this study, the thematic structure of GenAI research in the field of education during the 2023–2025 period was examined. Given the need to work with a large-scale dataset comprising 15,036 studies, a Latent Dirichlet Allocation (LDA)-based topic modelling analysis was selected. A search conducted on Scopus resulted in the inclusion of 15,036 studies in the analysis. The LDA analysis identified 12 prominent topics based on consistency scores. Descriptively, these prominent topics were grouped under four broad thematic areas. Among these axes, the technical axis holds the largest share (25%). It was observed that whilst technical topics were on the rise during the 2023–2025 period, topics such as ethics and chatbots showed a relative downward trend. The observed trends suggest that scholarly attention is increasingly directed towards technically oriented research. The findings also indicate a need for theoretical frameworks to ensure pedagogical depth. It is anticipated that the study’s findings will serve as an important reference point for future research. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). | |
| dc.description.sponsorship | Fimath | |
| dc.description.sponsorship | rat University Scientific Research Projects Unit [KRMYO.26.01] | |
| dc.identifier.articlenumber | 21582440261492792 | |
| dc.identifier.doi | 10.1177/21582440261492792 | |
| dc.identifier.issn | 2158-2440 | |
| dc.identifier.issue | 4 | |
| dc.identifier.scopus | 2-s2.0-105052943452 | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/munzur/1130 | |
| dc.identifier.volume | 16 | |
| dc.identifier.wos | WOS:001900043800001 | |
| dc.language.iso | en | |
| dc.publisher | SAGE Publications Inc. | |
| dc.relation.ispartof | SAGE Open | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | GenAI education | |
| dc.subject | Generative AI | |
| dc.subject | LDA | |
| dc.subject | thematic analysis | |
| dc.subject | topic modelling | |
| dc.subject.classification | Social Sciences, Interdisciplinary | |
| dc.subject.classification | Social Sciences - Other Topics | |
| dc.subject.other | AI | |
| dc.subject.other | PUBLICATIONS | |
| dc.title | Generative Artificial Intelligence in Education: Conceptual Dimensions, Trends, and Research Directions | |
| dc.type | Article | |
| rdl.citation.openalex | 0 | |
| rdl.citation.scopus | 0 | |
| rdl.citation.wos | 0 | |
| rdl.oa.status | gold | |
| rdl.oa.url | https://doi.org/10.1177/21582440261492792 | |
| rdl.scopus.quartile | Q1 | |
| rdl.sdg | SDG04 | |
| rdl.source.index | SCOPUS | |
| rdl.source.index | WOS | |
| rdl.wos.index | Social Science Citation Index (SSCI) |

