Backend
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
arXiv cs.IR0 views
arXiv:2610.36181v1 Announce Type: new 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 The analysis follows an unpublished plan whose timing cannot be verified, so it is not presented as pre-registered.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.
Source:https://arxiv.org/abs/2610.36181PreviousWhen Connectivity Misleads: Guiding Agentic Search with Contrastive Evidence PathsNextCounterfactual Profile Perturbations for Recommendation Explanations: A Reproducibility Study
Related posts
DeepSeek's Open-Source "Super-Agent" Codes Without Being Asked
0dev.to GitHub
All-in-one platform or a stack you assemble? Where do coaches and educators land?
0dev.to Discuss
I Wanted to Live on Coruscant. PAT Scared Me. Now My Alexa Lives on Mute.
0dev.to Discuss
Detect What You Can Name: PaliGemma 2 Object Detection with Transformers
0dev.to Machine Learning
When Should an LLM Router Stop Being Sticky? llm-d 1.0 Cache-Affinity Routing, Break-Even Math, and a Simulation
0dev.to Machine Learning

Understanding HackerTube: Karpathy’s Two-Hour Experiment in Better Recommendations
0dev.to Machine Learning