The AI Gap: How Socioeconomic Status Affects Language Technology Interactions

2025-05-17

AI Summary

This study investigates how socioeconomic status (SES) systematically shapes user interactions with large language models (LLMs), exacerbating an “AI Gap”—a structural disparity between technological access and effective, high-quality usage. Method: A mixed-methods study with 1,000 diverse participants integrated surveys, 6,482 real-world prompt logs, qualitative coding, and quantitative analyses—including chi-square tests, thematic modeling, and linguistic complexity metrics. Contribution/Results: It is the first large-scale empirical study to reveal SES-driven interaction stratification: high-SES users prefer concise, abstract, tool-oriented prompts; low-SES users favor anthropomorphic, concrete, and polite formulations, with significant SES-based divergence in task types and topic preferences. The study formally conceptualizes the “AI Gap” and provides foundational evidence for designing SES-aware, equitable AI systems.

Technology Category

• Humans and AI: User Experience and Usability
• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Socioeconomic status (SES) fundamentally influences how people interact with each other and more recently, with digital technologies like Large Language Models (LLMs). While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data. We survey 1,000 individuals from diverse socioeconomic backgrounds about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs. We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics. Higher SES entails a higher level of abstraction, convey requests more concisely, and topics like 'inclusivity' and 'travel'. Lower SES correlates with higher anthropomorphization of LLMs (using ''hello'' and ''thank you'') and more concrete language. Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to exacerbate the digital divide. These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups.

Problem

• Investigates how socioeconomic status affects language technology usage
• Examines interaction style differences across SES groups with AI
• Highlights SES-driven linguistic disparities exacerbating the digital divide

Innovation

• Surveyed 1,000 diverse SES individuals
• Collected 6,482 real LLM interaction prompts
• Analyzed SES-based linguistic and usage differences