metadata
license: mit
language:
- en
size_categories:
- 1K<n<10K
The AI Gap: How Socioeconomic Status Affects Language Technology Interactions
Dataset Summary
This dataset comprises responses from 1,000 individuals from diverse socioeconomic backgrounds, collected to study how socioeconomic status (SES) influences interaction with language technologies, particularly generative AI and large language models (LLMs). Participants shared demographic and socioeconomic data, as well as up to 10 real prompts they previously submitted to LLMs like ChatGPT, totaling 6,482 unique prompts.
Dataset Structure
The dataset is provided as a single CSV file:
survey_language_technologies.csv
| Column Name | Description |
|---|---|
id |
Anonymized respondent ID |
gender |
Gender identity (Male, Female, Non-binary, Other, Prefer not to say) |
gender_other |
Custom gender identity if "Other" was selected |
age |
Age group (e.g., 18–24, 25–34, etc.) |
nationality |
One or more nationalities (semicolon-separated) |
ethnicity |
One or more ethnic identities (semicolon-separated) |
ethnicity_other |
Custom ethnicity if "Other" was selected |
marital |
Marital status |
marital_other |
Custom marital status if "Other" was selected |
language |
First language(s) (semicolon-separated) |
language_other |
Custom language if "Other" was selected |
religion |
Religious affiliation |
religion_other |
Custom religion if "Other" was selected |
education |
Participant’s highest education level |
mum_education |
Mother's highest education level |
dad_education |
Father's highest education level |
ses |
Self-assessed SES on a 1–10 ladder scale |
home |
Home ownership status (Own, Rent, Other) |
home_other |
Custom home ownership type if "Other" was selected |
employment |
Current employment status |
occupation |
Current or most recent job (semicolon-separated if multiple) |
mother_occupation |
Mother's occupation(s) |
father_occupation |
Father's occupation(s) |
hobbies |
Hobbies and free-time activities (semicolon-separated) |
hobbies_other |
Custom hobbies if "Other" was selected |
tech |
Daily-used digital devices (semicolon-separated) |
tech_other |
Custom digital devices if "Other" was selected |
know_nlp |
NLP tools the user is familiar with (semicolon-separated) |
know_nlp_other |
Custom tools if "Other" was selected |
use_nlp |
NLP tools the user has used (semicolon-separated) |
use_nlp_other |
Custom tools if "Other" was selected |
would_nlp |
NLP tools find useful but not used because of scance performance (semicolon-separated) |
would_nlp_other |
Custom tools if "Other" was selected |
frequency_llm |
Frequency of LLM use (Every day, Nearly every day, Sometimes, Rarely, Never) |
llm_use |
LLMs used (e.g., ChatGPT, Claude, Bard, etc.) |
llm_other |
Custom LLMs if "Other" was selected |
usecases |
Tasks performed with LLMs (e.g., Writing, Learning, Coding, etc.) |
usecases_other |
Custom tasks if "Other" was selected |
contexts |
Contexts in which LLMs are used (e.g., Work, Personal, School) |
contexts_other |
Custom context if "Other" was selected |
prompt1–prompt10 |
Up to 10 prompts submitted by the participant to any AI chatbot |
comments |
Open-ended user comments |
Note: All multi-select fields are semicolon (
;) separated.
Citation
If you use this dataset in your research, please cite the associated paper:
@inproceedings{bassignana-2025-survey,
title = "The {AI} {G}ap: {H}ow {S}ocioeconomic {S}tatus {A}ffects {L}anguage {T}echnology {I}nteractions",
author = "Bassignana, Elisa and Cercas Curry, Amanda and Hovy, Dirk",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics",
year = "2025",
url = "https://arxiv.org/abs/2505.12158"
}
Dataset Curators
- Elisa Bassignana (IT University of Copenhagen)
- Amanda Cercas Curry (CENTAI Institute)
- Dirk Hovy (Bocconi University)
Links
- 📂 Dataset file:
survey_language_technologies.csv - 📄 Survey Interface (may take some time to load): https://nlp-use-survey.streamlit.app/
- 📝 Paper (preprint): https://arxiv.org/abs/2505.12158