By Date

Most workers who will be exposed to artificial intelligence (AI) will not require specialised AI skills (e.g. machine learning, natural language processing, etc.). Even so, AI will change the tasks these workers do, and the skills they require. This report provides first estimates for the effect of artificial intelligence on the demand for skills in jobs that do not require specialised AI skills. The results show that the skills most demanded in occupations highly exposed to AI are management and business skills. These include skills in general project management, finance, administration and clerical tasks. The results also show that there have been increases over time in the demand for these skills in occupations highly exposed to AI. For example, the share of vacancies in these occupations that demand at least one emotional, cognitive or digital skill has increased by 8 percentage points. However, using a panel of establishments (which induces plausibly exogenous variation in AI exposure), the report finds evidence that the demand for these skills is beginning to fall.

This paper looks at the links between AI and wage inequality across 19 OECD countries. It uses a measure of occupational exposure to AI derived from that developed by Felten, Raj and Seamans (2019) – a measure of the degree to which occupations rely on abilities in which AI has made the most progress. The results provide no indication that AI has affected wage inequality between occupations so far (over the period 2014-2018). At the same time, there is some evidence that AI may be associated with lower wage inequality within occupations – consistent with emerging findings from the literature that AI reduces productivity differentials between workers. Further research is needed to identify the exact mechanisms driving the negative relationship between AI and wage inequality within occupations. One possible explanation is that low performers have more to gain from using AI because AI systems are trained to embody the more accurate practices of high performers. It is also possible that AI reduces performance differences within an occupation through a selection effect, e.g. if low performers leave their job because they are unable to adapt to AI tools by shifting their activities to tasks that AI cannot automate.

Engaging with research, and ensuring research evidence is used well, is key to professionalising education policy making processes, and ultimately to improving educational outcomes. But the systematic use of evidence in policy making faces many challenges. This policy brief draws on evidence from the OECD Strengthening the Impact of Education Research project’s country learning seminars, as well as the project’s policy survey that collected responses from ministries of education in 37 education systems from 29 countries. The project is based in the OECD’s Centre for Educational Research and Innovation (CERI). This brief presents a set of case studies on two questions: • What human resource strategies can build individual and collective civil service professionalism? • What stable structures and mechanisms can contribute to the systematic and thoughtful use of evidence in policy processes?

Le Maroc a atteint la scolarisation universelle au niveau du primaire, et a connu une croissance significative de la scolarisation dans l'enseignement secondaire du premier et du deuxième cycle. Cependant, des problèmes subsistent quant à la qualité des apprentissages. Les élèves marocains de 15 ans figurent parmi les moins performants des pays participants à l’enquête du Programme international pour le suivi des acquis des élèves (PISA) de 2018, bien en dessous de la moyenne de l'OCDE et également en dessous des autres pays de la région Moyen-Orient et Afrique du Nord (MENA). Les réformes éducatives en cours visent à responsabiliser les établissements et à en faire des acteurs centraux de l’amélioration de la qualité de l’ensemble du système éducatif. Ce rapport vise à fournir au Maroc les outils nécessaires pour la mise en œuvre d'un système d'autoévaluation, de suivi et de soutien externe des établissements scolaires. Il propose un cadre d'indicateurs de la qualité qui peut fournir une vision commune et cohérente de ce qui constitue une « bonne école », et fournit des recommandations pour que les processus d’autoévaluation ainsi que le suivi et le soutien apporté aux établissements se concentrent sur l’essentiel.

This paper reviews a number of previous studies that have investigated how measure of non-cognitive skills predict important life outcomes such as educational attainment, employment, earnings, and self-reported health and life satisfaction. All reviewed studies analyse data from large-scale surveys from multiple countries and rely on the Big-Five framework to assess non-cognitive skills. The paper finds that measures of non-cognitive skills are robustly and consistently associated to indicators of life success in youth and adulthood, and have incremental predictive power over traditional measures of cognitive ability.

Generative artificial intelligence (AI) presents myriad opportunities for integrity actors—anti-corruption agencies, supreme audit institutions, internal audit bodies and others—to enhance the impact of their work, particularly through the use of large language models (LLMS). As this type of AI becomes increasingly mainstream, it is critical for integrity actors to understand both where generative AI and LLMs can add the most value and the risks they pose. To advance this understanding, this paper draws on input from the OECD integrity and anti-corruption communities and provides a snapshot of the ways these bodies are using generative AI and LLMs, the challenges they face, and the insights these experiences offer to similar bodies in other countries. The paper also explores key considerations for integrity actors to ensure trustworthy AI systems and responsible use of AI as their capacities in this area develop.

Young people from low socio-economic status (SES) backgrounds face additional barriers as they seek to convert their qualifications and experience into successful employment. They encounter particular challenges in seeking to enter high status jobs. The barriers they face can be productively conceptualised in terms of economic, human, social and cultural capital accumulation. Schools can help to build these resources through programmes of career guidance, but to be successful they must actively respond to predictable barriers relating to access to trusted information and useful experiences. PISA shows a need for socially focused interventions. Career uncertainty and confusion is shaped by SES. Low SES students are also less likely to engage in most commonplace career development activities. Equitable guidance systems will target greater provision at low SES students and aim ultimately to provide personalise provision to all students, encouraging and enabling understanding of and progression towards careers promising greatest personal fulfilment. Insights from longitudinal data provide new opportunities for more scientific and strategic approaches to delivering effective provision.

This paper explores enhancements to international large-scale assessments (ILSAs). It advocates for diversification, targeting specific groups or individuals for more precise diagnoses, and flexibilisation, refining the item bank for assessments' relevance and adaptability. The paper also introduces prototypes for new assessment tools, representing a significant evolution in ILSAs' design and application, aiming for broader impact and increased adaptability in ILSAs.

This report presents the conceptual foundations of the OECD Programme for International Student Assessment (PISA) Vocational Education and Training (VET), currently in the Development Phase of implementation which aims to provide a comprehensive and rigorous international survey of student knowledge and skills that are essential for success in selected occupational areas. The PISA-VET assessment covers professional knowledge and skills in five occupational areas (automotive technician, business and administration, electrician, nursing/healthcare assistant and hotel receptionist), plus an evaluation of learners’ employability skills, including literacy, problem solving, task performance (conscientiousness) and collaboration with others. This publication includes the frameworks for assessing all the knowledge and skills included in the assessment. These chapters outline the content knowledge and skills that learners need to acquire in each domain, how each domain is assessed, and the contexts in which this knowledge and these skills are applied. The publication also presents the frameworks for the various questionnaires distributed to students, principals of VET institutions, teachers and trainers, including a questionnaire for trainers in work-based learning environments and a system level data questionnaire for participating countries. The questionnaires are also included as an annex to the publication.

In the 2023 Survey of Adult Skills (PIAAC), the Big Five personality traits were assessed using the BFI-2-XS, the 15-item extra-short form of the Big Five Inventory-2 (BFI-2). For this purpose, the instrument was translated into 24 languages and adapted to 29 countries, resulting in 39 language versions. This translation and adaptation process followed state-of-the-art procedures to generate language versions of the BFI-2-XS that are maximally comparable across countries and regions. In the present paper, we describe this general translation procedure from a methodological point of view. We also document each resulting language version and report in detail the decisions taken during the translation process and the adaptations made to preexisting national versions of the BFI-2-XS. Our aim is to share with researchers the resulting BFI-2-XS language versions developed with high quality standards to allow maximal cross-cultural comparability. Our intention in so doing is to enable their wider usage beyond PIAAC.