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.

Large Language Models (LLMs) have shown capabilities close to human performance in various analytical tasks, leading researchers to use them for time and labor-intensive analyses. However, their capability to handle highly specialized and open-ended tasks in domains like policy studies remains in question. This paper investigates the efficiency and accuracy of LLMs in specialized tasks through a structured user study focusing on Human-LLM partnership. The study, conducted in two stages—Topic Discovery and Topic Assignment—integrates LLMs with expert annotators to observe the impact of LLM suggestions on what is usually human-only analysis. Results indicate that LLM-generated topic lists have significant overlap with human generated topic lists, with minor hiccups in missing document-specific topics. However, LLM suggestions may significantly improve task completion speed, but at the same time introduce anchoring bias, potentially affecting the depth and nuance of the analysis, raising a critical question about the trade-off between increased efficiency and the risk of biased analysis.

Media framing is the study of strategically selecting and presenting specific aspects of political issues to shape public opinion. Despite its relevance to almost all societies around the world, research has been limited due to the lack of available datasets and other resources. This study explores the possibility of dataset creation through crowdsourcing, utilizing non-expert annotators to develop training corpora. We first extend framing analysis beyond English news to a multilingual context (12 typologically diverse languages) through automatic translation. We also present a novel benchmark in Bengali and Portuguese on the immigration and same-sex marriage domains.Additionally, we show that a system trained on our crowd-sourced dataset, combined with other existing ones, leads to a 5.32 percentage point increase from the baseline, showing that crowdsourcing is a viable option. Last, we study the performance of large language models (LLMs) for this task, finding that task-specific fine-tuning is a better approach than employing bigger non-specialized models.