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The AI Gap: How Socioeconomic Status Affects Language Technology Interactions

Paper · 2025 · Elisa Bassignana, Amanda Cercas Curry and Dirk Hovy

Summary

Socioeconomic status (SES) shapes how people communicate, but earlier studies linking SES to language technology relied on proxy measures or synthetic text. This paper asks whether people from different SES backgrounds use language technologies, and LLM chatbots in particular, in systematically different ways. The authors ran a survey on Prolific with 1,000 U.S. and U.K. participants. Each person placed themselves on the Macarthur scale, answered questions about demographics and technology habits, and submitted up to ten of their recent chatbot requests. This produced 6,482 real prompts.

The analysis finds statistically significant gaps between groups. Higher-SES respondents own more devices, use chatbots more often, and apply them to technical, work-related tasks. Their prompts are also shorter and more abstract. Lower-SES respondents write more concrete prompts and treat the model more like a person, for example by greeting and thanking it.

The authors argue these patterns could deepen the digital divide, which they call the "AI Gap". They also note that existing benchmarks may not reflect the tasks typical of lower-SES users, so SES should be considered when building and evaluating models.

Key points

  • Survey design: The survey had three parts: 17 sociodemographic questions (including self-placement on the Macarthur scale), questions on language technology and LLM usage, and collection of each participant's last ten chatbot requests.
  • Data quality: Three pilot rounds (20, 20 and 79 people) refined the survey, which was hosted in a custom streamlit app. Fewer than 2.5% of participants gave answers that conflicted with their Prolific profile.
  • Released dataset: The prompts are published on Hugging Face as MilaNLProc/survey-language-technologies. The authors present it as the first collection of real prompts annotated with fine-grained sociodemographic data, including SES.
  • Usage and prompt differences: Higher-SES users use chatbots more for coding, data analysis and writing, and their prompts more often concern topics like inclusivity and travel. Prompt length and concreteness differ significantly across SES groups.
  • Search-style questions: Questions containing wh-words or "how" make up 46.6% (low), 43.5% (middle) and 45.4% (upper SES) of prompts. This suggests partial replacement of search engines by chatbots.
  • Limitations: The sample consists only of U.S. and U.K. crowdworkers, who are likely more tech-savvy than the general population. SES is measured subjectively.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Bocconi UniversityMilan · Italy28 works · 3,077 citations🎓IT University of CopenhagenCopenhagen · Denmark27 works · 2,406 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSafety Research · 543 works

Cite

Cite as Elisa Bassignana, Amanda Cercas Curry and Dirk Hovy. 2025. The AI Gap: How Socioeconomic Status Affects Language Technology Interactions. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.914

@inproceedings{bassignana2025ai,
  title = {{The AI Gap: How Socioeconomic Status Affects Language Technology Interactions}},
  author = {Elisa Bassignana, Amanda Cercas Curry and Dirk Hovy},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.914},
  url = {https://doi.org/10.18653/v1/2025.acl-long.914}
}

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