Education gap smaller in generative AI use than early internet use
Is the education gap in generative AI use smaller than the internet's was at a similar level of use? Evidence from 16 European countries
Information RetrievalComputers and Society
Summary
People with more education usually start using new technologies first. This paper compares how big the education gap was when people started using the internet to the gap now with generative AI use in Europe. The authors found that, in most countries studied, the difference in generative AI use between those with high and low education levels is smaller than it was for the internet at similar levels of use. However, this finding only includes some countries and doesn’t prove cause and effect.
What this means in practice
- •For policy makers: Design targeted digital inclusion programs by understanding smaller education gaps in generative AI adoption compared to early internet use in Europe.
- •For telecommunications planners: Plan technology outreach and infrastructure investments considering faster or more equal generative AI adoption patterns than initial internet rollout.
Authors
Keisuke Watanabe
Abstract
New technologies are usually adopted first by people with more formal education. This paper asks whether the education gap in generative AI use in 2025 is smaller or larger than the internet's gap in the same country in the year when overall internet use was at a similar level. It uses public Eurostat survey data on ICT usage by individuals aged 16 to 74. Each country's 2025 rate of generative AI use in the last three months is matched to the internet year whose overall last-three-months use was closest, within 5 percentage points, and the gap between people with high (ISCED 5 to 8) and low (ISCED 0 to 2) completed education is measured as a log ratio and as a percentage-point difference. Sixteen of 35 countries could be matched; most of the others were early internet adopters. In the matched countries, the generative AI gap was smaller in 13 of 16 countries on the log ratio (median change -0.625, 95% bootstrap interval -0.969 to -0.291, two-sided sign test p = 0.021) and in 15 of 16 on the percentage-point gap (median change -27.6 points, interval -32.2 to -16.8, p = 0.0005). The point-gap result keeps its sign in every generative-AI-versus-internet sensitivity analysis with 8 or more countries, including comparisons restricted on both sides to ages 25 to 54 and 55 to 74, but its size is sensitive to matching that takes education composition into account (about -10 to -11 points in 7 and 8 countries). The ratio result is fragile: it fails the decision rule when countries matched at their first internet observation are removed (9 of 11, p = 0.065). The analysis follows an unpublished plan whose timing cannot be verified, so it is not presented as pre-registered. The results describe unadjusted education contrasts in a non-random set of countries, not an education effect, and do not generalise to the EU as a whole.