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Is Education Losing the Race with Technology?

AI's Progress in Maths and Reading

image of Is Education Losing the Race with Technology?

Advances in artificial intelligence (AI) are ushering in a large and rapid technological transformation. Understanding how AI capabilities relate to human skills and how they develop over time is crucial for understanding this process.

In 2016, the OECD assessed AI capabilities with the OECD’s Survey of Adult Skills (PIAAC). The present report follows up the earlier study, collecting expert judgements in 2021 on whether computers can solve the PIAAC literacy and numeracy tests. It is part of a comprehensive ongoing project on assessing AI.

This study shows that AI could potentially outperform large shares of the population on PIAAC – 90% of adults in literacy and 57-88% of adults in numeracy. AI’s literacy capabilities had improved considerably since the 2016 assessment. According to experts, AI will solve the entire literacy and numeracy tests by 2026.

These findings have important implications for employment and education. Large shares of the workforce use literacy and numeracy skills daily at work with a proficiency comparable or below that of computers. AI could affect the literacy- and numeracy-related tasks of these workers. In this context, education systems should strengthen the foundation skills of students and workers and teach them to work together with AI.

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Setting the stage: Approaches to assessing AI’s impact

This chapter introduces the study and situates it in a broader context of related research. The study assesses artificial intelligence (AI) capabilities by collecting expert judgements on whether AI can carry out tests from the OECD’s Survey of Adult Skills of the Programme for International Assessment of Adult Competencies (PIAAC). It follows up an earlier study from 2016 to track changes in AI capabilities with regard to PIAAC over time. The chapter first provides an overview of past studies that assess computer capabilities and their impact on the economy. Against this background, it presents the objectives of this study and discusses potential strengths and weaknesses of the methodological approach the study uses to assess AI. The chapter concludes with an outline of the structure of this report.

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