@inproceedings{akter-etal-2025-costs,
    title = "Costs and Benefits of {AI}-Enabled Topic Modeling in {P}-20 Research: The Case of School Improvement Plans",
    author = "Akter, Syeda Sabrina  and
      Hunter, Seth  and
      Woo, David  and
      Anastasopoulos, Antonios",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bea-1.34/",
    pages = "460--476",
    ISBN = "979-8-89176-270-1",
    abstract = "As generative AI tools become increasingly integrated into educational research workflows, large language models (LLMs) have shown substantial promise in automating complex tasks such as topic modeling. This paper presents a user study that evaluates AI-enabled topic modeling (AITM) within the domain of P-20 education research. We investigate the benefits and trade-offs of integrating LLMs into expert document analysis through a case study of school improvement plans, comparing four analytical conditions. Our analysis focuses on three dimensions: (1) the marginal financial and environmental costs of AITM, (2) the impact of LLM assistance on annotation time, and (3) the influence of AI suggestions on topic identification. The results show that LLM increases efficiency and decreases financial cost, but potentially introduce anchoring bias that awareness prompts alone fail to mitigate."
}