# Gender, Age, and Technology Education Influence the Adoption and Appropriation of LLMs

arXiv: https://arxiv.org/abs/2310.06556 · HTML: https://mlsanity.com/paper/2310.06556/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2023.

## Built on by (most cited first, 10 of 10)

- [LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions](https://mlsanity.com/paper/2411.05025/) (2024)
- [MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation](https://mlsanity.com/paper/2407.01972/) (2024)
- [The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models](https://mlsanity.com/paper/2404.08760/) (2024)
- [Do Prompt Patterns Affect Code Quality? A First Empirical Assessment of ChatGPT-Generated Code](https://mlsanity.com/paper/2504.13656/) (2025)
- [Being Kind Isn't Always Being Safe: Diagnosing Affective Hallucination in LLMs](https://mlsanity.com/paper/2508.16921/) (2025)
- [WebANNS: Fast and Efficient Approximate Nearest Neighbor Search in Web Browsers](https://mlsanity.com/paper/2507.00521/) (2025)
- [Shopping with a Platform AI Assistant: Who Adopts, When in the Journey, and What For](https://mlsanity.com/paper/2603.24947/) (2026)
- [How Early Adopters Used Generative AI Worldwide: Variation by Country Income and Language](https://mlsanity.com/paper/2605.30685/) (2026)
- [AI Literacy: An Exercise in Power-Knowledge](https://mlsanity.com/paper/2607.27547/) (2026)
- [When the AI Leaves the Tailorshop: Measuring What an LLM Advisor Leaves Behind in Complex Problem Solving](https://mlsanity.com/paper/2610.00163/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) such as ChatGPT have become increasingly integrated into critical activities of daily life, raising concerns about equitable access and utilization across diverse demographics. This study investigates the usage of LLMs among 1,500 representative US citizens. Remarkably, 42% of participants reported utilizing an LLM. Our findings reveal a gender gap in LLM technology adoption (more male users than female users) with complex interaction patterns regarding age. Technology-related education eliminates the gender gap in our sample. Moreover, expert users are more likely than novices to list professional tasks as typical application scenarios, suggesting discrepancies in effective usage at the workplace. These results underscore the importance of providing education in artificial intelligence in our technology-driven society to promote equitable access to and benefits from LLMs. We urge for both international replication beyond the US and longitudinal observation of adoption.