Abstract
This exploratory study investigates AI-related discourse in practitioner-oriented IT conferences. The analysis covers presentations from six conferences in Poland and the United States between 2018 and 2025. AI-related presentations were identified and classified into seven AI subdisciplines using a hybrid text classification approach. The results show a strong increase in AI-related topics after 2022, with higher levels of interest observed in the United States. Machine Learning emerged as the dominant subdiscipline and the central element of the AI ecosystem. The findings demonstrate that industry conferences can provide valuable insights into technological trends and the evolution of AI within professional communities.
Paper Type
Poster
DOI
10.62036/ISD.2026.162
Tracking AI Trends Across Subdisciplines: A Multi‑Year Analysis of Industry Conference Content
This exploratory study investigates AI-related discourse in practitioner-oriented IT conferences. The analysis covers presentations from six conferences in Poland and the United States between 2018 and 2025. AI-related presentations were identified and classified into seven AI subdisciplines using a hybrid text classification approach. The results show a strong increase in AI-related topics after 2022, with higher levels of interest observed in the United States. Machine Learning emerged as the dominant subdiscipline and the central element of the AI ecosystem. The findings demonstrate that industry conferences can provide valuable insights into technological trends and the evolution of AI within professional communities.
Recommended Citation
Balsamski, B., Blachnicka, K. & Stal, J.(2026). Tracking AI Trends Across Subdisciplines: A Multi‑Year Analysis of Industry Conference Content. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.162