Potential Abstract:
In recent years, large language models (LLMs) have gained significant attention for their capabilities in generating human-like text. However, the use of LLMs in educational settings raises important questions about the potential political implications of the outputs they produce. This empirical study explores how anchors embedded in LLMs can influence the generation of text and shape political attitudes among users, particularly in educational contexts. We employ causal models to examine the direct and indirect effects of anchors in LLM-generated text on political beliefs and behaviors. By analyzing a large dataset of LLM-generated text and survey responses, we identify key anchor patterns that are associated with shifts in political attitudes. Our findings suggest that the presence of certain anchors in LLM-generated text can significantly influence individuals’ political views, highlighting the need for critical examination of the ethical implications of using LLMs in educational settings. This study contributes to the growing body of literature on the intersection of artificial intelligence and education, shedding light on the ways in which LLMs can shape political discourse and potentially impact democratic processes.
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