This document is about the guidance for policy-makers on leveraging the opportunities and addressing the risks of the growing connection between AI and education, with a focus on achieving the Sustainable Development Goal.
The document is a publication by UNESCO titled “AI and education: Guidance for policy-makers.” It provides an overview of the potential of artificial intelligence (AI) in education and offers guidance for policy-makers on how to leverage the opportunities and address the risks associated with AI in education.
The document begins by highlighting the importance of education as a basic human right and the foundation for peace and sustainable development. It introduces the Global Education 2030 Agenda, which aims to ensure inclusive and equitable quality education for all. AI is seen as a potential tool to address the challenges in education and accelerate progress towards the Sustainable Development Goal.
The publication emphasizes the need for policy-makers to understand the essentials of AI, including its definitions, techniques, and technologies. It explains that AI is an interdisciplinary field that involves the use of computer systems to imitate human intelligence. The document provides a brief introduction to AI techniques such as classical AI, machine learning, artificial neural networks, and deep learning.
The document also discusses the emerging trends and implications of AI in education. It explores how AI can be leveraged to enhance education, including its use in education management and delivery, learning and assessment, and teacher empowerment. It highlights the importance of ensuring the ethical, inclusive, and equitable use of AI in education and the need to prepare humans to live and work with AI.
The challenges of harnessing AI to achieve SDG 4 are also addressed in the publication. It discusses issues such as data ethics and algorithmic biases, gender-equitable AI, monitoring and evaluation of AI in education, and the impact of AI on teacher roles and learner agency.
The document reviews policy responses to AI in education and identifies common areas of concern. It emphasizes the need for a system-wide vision and strategic priorities, interdisciplinary planning, and inter-sectoral governance. It also provides policy recommendations for policy-makers, including the development of policies and regulations for the ethical use of AI, the integration of AI into education management and teaching, and the fostering of local AI innovations for education.
Overall, the document highlights the potential of AI in education and guides policy-makers on how to leverage this potential while addressing the challenges and risks associated with AI. It emphasizes the importance of ensuring the ethical and inclusive use of AI in education and the need for collaboration and cooperation among stakeholders in the field of AI and education.
The 6 key principles that should guide the deployment of AI in education
1. Inclusion and Equity
AI should be used in a way that promotes equal access and opportunities for all learners, regardless of their background or abilities. Policies and practices should ensure that AI is not used to perpetuate or exacerbate existing inequalities in education.
2. Ethical Use
AI in education should be guided by ethical principles, ensuring that it respects privacy, data protection, and human rights. It should be transparent, accountable, and explainable, with clear guidelines on how data is collected, used, and stored.
3. Human-Centered Approach
AI should enhance and support human teachers and learners, rather than replace them. It should be designed to empower teachers, improve learning outcomes, and enhance the overall educational experience.
4. Lifelong Learning
AI should be used to promote lifelong learning opportunities for all individuals, enabling them to acquire new skills and adapt to the changing demands of the AI era. Education systems should prepare learners to live and work effectively with AI.
5. Evidence-Based Decision-Making
The deployment of AI in education should be based on sound research and evidence. Policies and practices should be informed by rigorous evaluation and monitoring, ensuring that AI interventions are effective and beneficial for learners.
6. Collaboration and Stakeholder Engagement
The development and implementation of AI in education policies should involve collaboration and consultation with all relevant stakeholders, including educators, learners, parents, policymakers, and technology experts. This collaborative approach will help ensure that AI is aligned with the needs and values of the education community.
These principles are essential to ensure that AI in education is used responsibly and effectively, maximizing its potential to improve learning outcomes and promote equitable access to quality education.
Some core AI techniques
Machine learning
This technique involves analyzing large amounts of data to identify patterns and build a model that can be used to predict future values. It includes supervised learning, unsupervised learning, and reinforcement learning.
Deep learning
This technique refers to artificial neural networks with multiple intermediary layers. It has led to many recent advancements in AI, such as natural language processing, speech recognition, computer vision, and image creation.
Artificial neural networks (ANN)
ANNs are AI approaches inspired by the structure of biological neural networks. They consist of interconnected layers of artificial neurons and are trained to compute outputs for new data.
Generative adversarial networks (GAN)
GANs involve two deep neural networks competing against each other, with one generating possible outputs and the other evaluating those outputs. This approach has been used for image manipulation and creating realistic but fake images.
Symbolic AI or rule-based AI
This approach involves writing sequences of rules and conditional logic for the computer to follow in order to complete a task. It has been used in expert systems and knowledge engineering.
Natural language processing (NLP)
NLP uses AI to interpret and generate texts, including semantic analysis and speech recognition. It is used in applications such as auto-journalism, translation, and virtual assistants.
Image recognition and processing
AI is used for tasks such as facial recognition, handwriting recognition, image manipulation, and autonomous vehicles. This involves techniques like deep learning and convolutional neural networks.
Reinforcement learning
This approach involves continuously improving an AI model based on feedback and rewards. It is used in applications where the AI needs to learn and evolve, such as autonomous vehicles.
It is important to note that these are just a few examples of core AI techniques, and there are many other techniques and approaches used in the field of AI.
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