Abstract

The available cross-country evidence on participation in adult education (AE) shows that the main beneficiaries of AE are those who already possess high levels of education and hold good positions in the labour market. The uneven share of educational goods indicated by the statistics is a major concern for supranational actors like the EU, OECD, and UNESCO, and little is known about the various mechanisms contributing to the mismatch. Despite the prevailing consensus about education’s enabling effects, the benefits of education, which are not easily measurable, often go undocumented. Thus, demonstrating how these positive effects function is difficult due to the lack of robust longitudinal studies. Notably, the widely used European and OECD analyses are based on survey data that is not consistent across time, making them unusable for monitoring the effect of education on the individual life-course. Currently, digitalisation and artificial intelligence (AI) have become powerful trends; there are increasing societal expectations towards the machineries of calculation and their ability to enhance productivity and transparency for better governance. As computer scientists are aware that the system output depends on quality input (pronouncing warnings of ‘garbage in, garbage out’ effects), the composition of data and the information therein that is exploited by the new technologies needs to be carefully explored.