Reports

The choice and diversity of upper secondary education create great potential to respond to the needs of Indigenous learners, and high stakes at this level make it critical to promote learners' success. Having historically been marginalized in educational settings around the world, it is essential that Indigenous students receive a supportive environment and the resources to succeed at this level. This policy brief provides reflections on the topic of supporting Indigenous learners in upper secondary education from a Peer Learning Discussion hosted by the OECD’s Above and Beyond: Transitions in Upper Secondary Education project. In December 2021, the project organised an informal discussion with participants from New Zealand and two provinces in Canada (Alberta and Manitoba), focusing on the systems’ experiences of supporting Indigenous learners at this level. This policy brief presents a summary of insights shared during this informal discussion, as well as background on the issues participants raised.

This evolving paper follows a first paper released in 2021 on 'National or regional curriculum frameworks and visualisations'. It presented a compilation of visualisations of curriculum frameworks, main competences and strategic schemes provided by countries and jurisdictions as part of the OECD Education 2030 curriculum analysis work. This paper presents a compilation of visualisations from conceptual frameworks that align with the OECD Learning Framework – OECD Learning Compass 2030, developed by inter-governmental, international organisations, non-governmental associations, or at the school or local level. The OECD Learning Compass 2030 positions itself as an overarching framework, with a taxonomy that serves as a common language for a multitude of audiences and contexts. The paper is an evolving document: new frameworks will be added and updated on a regular basis, in particular with frameworks of those schools, NPOs and other social partners who become part of the OECD Education 2030 multi-stakeholders’ group.

This Education Spotlight, the second in a series of three, presents key lessons and inspiring examples of policy and practice to inform how traditional higher education programmes, like bachelor’s degrees, can effectively cultivate competencies crucial for green and digital innovation. The Spotlight was prepared by the OECD Higher Education Policy Team as part of the Education and Innovation Practice Community (EIPC), an action of the European Union’s New European Innovation Agenda, flagship 4 on 'Fostering, attracting and retaining deep tech talent'. EIPC seeks to bring together peers from policy and practice to advance understanding of the competencies that can trigger and shape innovation for the digital and green transitions, and the mechanisms through which higher education can contribute to their development in secondary education, higher education, and adult upskilling and reskilling.

This paper explores the economics of Artificial Intelligence (AI), focusing on its potential as a new General-Purpose Technology that can significantly influence economic productivity and societal wellbeing. It examines AI's unique capacity for autonomy and self-improvement, which could accelerate innovation and potentially revive sluggish productivity growth across various industries, while also acknowledging the uncertainties surrounding AI's long-term productivity impacts. The paper discusses the concentration of AI development in big tech firms, uneven adoption rates, and broader societal challenges such as inequality, discrimination, and security risks. It calls for a comprehensive policy approach to ensure AI's beneficial development and diffusion, including measures to promote competition, enhance accessibility, and address job displacement and inequality.

If test-takers do not engage with the assessment, the reliability of test scores and the validity of inferences about their proficiency may suffer. Test-taker disengagement is particularly likely in low-stakes assessments and, according to prior research, for certain types of students. But levels of engagement may also be related to aspects that test developers can manipulate, such as item characteristics. This paper investigates which item characteristics are associated with two indicators of test-taker disengagement, rapid guessing and breakoffs, in an international assessment of reading. Analyses of data from almost 500 000 students from 67 countries and economies that took part in the 2018 Programme for International Student Assessment (PISA) show that rapid guessing was observed mainly on simple multiple-choice questions. Breakoffs were more likely in the presence of idiosyncratic selected-response formats, such as hot spot or matching tasks. Both rapid guessing and breakoffs were more frequent on tasks involving long and complex texts.

Students are much more than their grades. Beyond performing well in school, students must learn to manage their relationships with others, confront stress, find purpose in what they do, and deal with a series of factors oftentimes beyond their control – all of this, during a particularly sensitive period of their lives. How they do across all these dimensions of life shapes their well-being, which in turn affects their school performance and their life outcomes beyond school. In 2015, PISA broke new ground by including indicators of student well-being alongside traditional measures of academic performance. However, the data on student well-being often remain overshadowed by country and economy scores in mathematics, science, and reading - traditionally considered the primary outputs of PISA. This paper presents a proposal to increase the visibility and policy impact of PISA indicators on well-being, by organising them in thematic areas and presenting them through data visualisations that respond to the needs of different kinds of users. The proposed PISA dashboard on students’ well-being has the potential to offer policy makers, educators, parents, and other stakeholders a comparative perspective on how well schools are fostering the essential foundations for students to lead fulfilling lives.

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.