World

The digital transition is a major contemporary challenge that unevenly impacts the life chances of occupational classes and the well-being of individuals. The decline of the working class, driven by skill-based technological change, further provides additional arguments for examining the impact of digitalization on individuals’ chances from a class perspective. The intersections between social class and gender deserve attention in relation to adult education participation. This paper aims to account for both individual-level characteristics—occupational class and gender—and macro-level characteristics including digitalization, measured by the Digital Economy and Society Index (DESI), and inequality, measured by the Gini coefficient. Analyzing data from the European Social Survey, Round 10 (2021/2022), our results show that digital performance in a given country is positively associated with the probability of participation in adult education. Women in countries with higher levels of digital performance are more likely to participate in adult education. We found evidence for a positive interaction between DESI and lower-grade service class for women, whereas in the case of men, we found positive interaction terms between DESI and small business owners, skilled workers, and unskilled workers.

Education–employment mismatch represents a persistent structural issue across Europe, especially among young people. In line with the digital transformation, green transformation and population aging, new jobs are emerging every day, and some of the older jobs are disappearing. However, existing skills of job seekers may not fit these new jobs. This article presents results from the EMLT + AI project, which aimed to explore how artificial intelligence (AI) tools could contribute to reducing such mismatches and supporting inclusive labor market integration. Based on a sample of 1039 participants across European countries, we analyzed the alignment between individuals’ educational background and their current employment, as well as their willingness to reskill. Using binary logistic regression models, the study identifies key factors influencing mismatch and reskilling motivation, including educational level, type of occupation, the presence of meaningful career guidance, and AI-based job search practices. The results indicate that individuals who hold a master’s degree and work in positions requiring at least bachelor’s level degrees are more likely to be matched with jobs that align with their field of study. However, access to mentoring remains limited. The paper concludes by proposing an AI-supported training model integrating career recommendation systems, flexible learning modules, and structured mentoring. These findings provide empirical evidence on how emerging technologies can foster more responsive and adaptive education-to-employment transitions, contributing to policy innovation and the development of inclusive digital labor ecosystems in Europe.

In many disaster-prone regions, lower-income communities face disproportionate impacts due to the prevalence of informal housing. Informal housing, characterized by substandard construction and lack of adherence to building codes, exacerbates vulnerabilities during disasters, leading to widespread destruction and hampered recovery efforts. This study examines the multifaceted causes of informal housing in Puerto Rico using a qualitative content analysis of applicable literature. Seven interdisciplinary factors were derived from 42 relevant manuscripts with identifiable factors linked to informal housing in Puerto Rico: Knowledge, Perception, Government Dynamics, Institutional Support, Enforcement, Culture, and Resources. Despite post-disaster efforts advocating for building back better, systemic challenges perpetuate informal housing practices, reinforcing cycles of vulnerability. This research underscores the need for integrated decision making in pre-disaster preparation and post-disaster reconstruction efforts. This research presents a detailed understanding of the Informal Housing Cycle, demonstrates how interdisciplinary factors are barriers to safe and sustainable housing, and explores the complex relationships between these factors. This study aims to guide policy and practice to reduce future disaster impacts on Puerto Rico housing, thus breaking the cycle of vulnerability, empowering communities, and fostering sustainable resilience in post-disaster reconstruction efforts.