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TEST SKILLS INTELLIGENCE

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What is Lorem Ipsum?
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

What is Lorem Ipsum?
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

AI and education: guidance for policy-makers

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AI and education: Guidance for policy-makers‘ is developed by UNESCO within the framework of the implementation of the Beijing Consensus, aimed at fostering AI-ready policy-makers in education. It aims to generate a shared understanding of the opportunities offered by AI for education, as well as its implications for the essential competencies required by the AI era.

AI in education is expected to be worth $6 billion by 2024 and has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards the United Nations SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks.

Policy-makers and educators have entered uncharted territory that raises fundamental questions on how the future of learning will interact with AI. The bottom line is that the deployment and use of AI in education must be guided by the core principles of inclusion and equity. For this to happen, policies must promote equitable and inclusive access to AI and the use of AI as a public good, with focus on empowering girls and women and disadvantaged socio-economic groups. The growing use of novel AI technologies in education will only benefit all of humanity if – by design – it enhances human-centred approaches to pedagogy, and respects ethical norms and standards. AI should be geared to improving learning for every student, empowering teachers and strengthening learning management systems. Beyond this, preparing students and all citizens to live and work safely and effectively with AI is a shared challenge at global level. Future learning and training systems must equip all people with core AI competencies, including understanding of how AI collects and can manipulate data, and skills to ensure safety and protection of personal data. Finally, AI by nature transcends the sectors, the planning of effective AI and education policies requires consultation and collaboration with stakeholders across disciplines and sectors.

This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts.

The state of the field of computational thinking in early childhood education (2022)

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The study is part of the OECD Education Working Papers and provides a deep dive on the state of computational thinking in early childhood education. Computer programming and associated Computational Thinking (CT) skills are essential to thriving in today’s academic and professional world.

There has been a growing focus globally on fostering CT skills as well as on introducing computer programming concepts and languages beginning as early as kindergarten and pre-primary school. Tools, curriculum, and frameworks to promote CT in the early years must be designed and  implemented in ways that engage children who cannot yet read and write, who learn through play, and who have a short attention span and limited working memory but also strong natural curiosity. This review summarises empirical and theoretical literature on the state of the field of CT as it relates to early learning and development, a time when young children are being introduced to foundational skills, such as literacy and numeracy, which can carefully be complemented by an exploration of CT.

The working paper begins with providing key definitions of terms related to CT and background on the field of CT. It goes on to discuss how CT found its place in learning standards and frameworks for early levels of education, as well as research on CT in early learning and development. Next, the review highlights various tools, technologies, and media that have been developed in the past decade for supporting CT in young children, including unplugged and screen-free interfaces. Finally, the review discusses the implementation of CT programmes in OECD countries and breaks down important issues of equity and access in CT education.

The structure of the working paper is divided as follows:

  1. Defining Computational thinking, Computer science, and programming
  2. Computational thinking frameworks and learning standards
  3. Computational thinking and early learning and development
  4. Tools for early CT learning
  5. Effective and scalable CT education
  6. Equity and access
  7. Concluding remarks and key takeaways for policymakers