Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)

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What Drives Generative AI Research? A Topic Modeling Investigation of Contemporary Literature

Authors
Yuhefizar1, Ronal Watrianthos1, *, Rita Komalasari2, Gilang Surendra1
1Politeknik Negeri Padang, Padang, Indonesia
2Politeknik LP3I, Bandung, Indonesia
*Corresponding author. Email: ronalwatrianthos@pnp.ac.id
Corresponding Author
Ronal Watrianthos
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-926-1_8How to use a DOI?
Keywords
BERTopic; ChatGPT; Generative Artificial Intelligence; Temporal Analysis; Topic Modeling
Abstract

This study presents a comprehensive analysis of the generative artificial intelligence research landscape during the transformative period of 2023-2024. Using BERTopic, we analyzed 1,314 scholarly articles retrieved from the Scopus database to identify thematic clusters and temporal evolution patterns. The analysis revealed 15 coherent topics, with educational applications emerging as the dominant theme (24.3%), followed by ethical implications (18.7%) and technical architecture (15.2%). Temporal analysis demonstrated a remarkable 628% increase in publication volume between 2023 and 2024, accompanied by significant shifts in research priorities. While technical architecture research declined by 53%, application-oriented topics experienced substantial growth, with educational applications increasing by 149%. The study identified six emerging themes, including multimodal generative AI (emergence score: 0.89), retrieval-augmented generation (0.84), and synthetic data generation (0.88). These findings indicate a paradigm shift from foundational technical research toward practical applications and societal impact assessment, providing valuable insights for researchers and policymakers navigating the rapidly evolving generative AI landscape.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)
Series
Advances in Engineering Research
Publication Date
31 December 2025
ISBN
978-94-6463-926-1
ISSN
2352-5401
DOI
10.2991/978-94-6463-926-1_8How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yuhefizar
AU  - Ronal Watrianthos
AU  - Rita Komalasari
AU  - Gilang Surendra
PY  - 2025
DA  - 2025/12/31
TI  - What Drives Generative AI Research? A Topic Modeling Investigation of Contemporary Literature
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)
PB  - Atlantis Press
SP  - 56
EP  - 64
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-926-1_8
DO  - 10.2991/978-94-6463-926-1_8
ID  - 2025
ER  -