AI can enhance education accessibility through personalized learning journeys and facilitating remote learning opportunities. In this chart we see the share of youth not in education, employment or training, total (% of youth population).

This perspective offers a real-time view of the worldwide news on education-related topics, providing us with a media perspective on the progress at the different fronts of SDG 4.

 

Developed in collaboration with 

A SDG Live Report is what it is all about! Here, you can have a real-time perspective of the state of the world in regard to AI and sustainability, tracking some of the main issues to address in the near future.

 

The impact of AI on the progress of Sustainable Development Goal 4 (SDG 4), which aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, has been transformative. Between 2015 and 2023, there have been 6,291,933 scientific publications examining the role of AI in education, demonstrating a strong academic interest in leveraging AI to enhance educational outcomes. These studies highlight AI’s potential to personalize learning experiences, provide real-time feedback, and support teachers in identifying students’ strengths and weaknesses. The media exposure, with 44,460 news articles, reflects significant public interest in AI’s ability to revolutionize education systems and address challenges such as access to quality education and resource allocation. Additionally, the development of 5,338 AI policies targeting SDG 4 indicates a concerted effort from policymakers to integrate AI into educational frameworks, ensuring that technological advancements contribute to equitable and inclusive learning opportunities.

In the next 5 to 10 years, we can expect AI to play an increasingly critical role in shaping the future of education. AI-driven adaptive learning platforms will become more prevalent, offering customized educational content that caters to individual learning styles and paces. These advancements will help bridge educational gaps, particularly in underserved communities, by providing access to quality educational resources and personalized support. Furthermore, AI’s ability to analyze vast amounts of data will enable more effective monitoring and evaluation of educational programs, informing evidence-based policy decisions. As AI technologies continue to evolve, collaboration between educational institutions, governments, and tech companies will be essential to ensure that AI-driven innovations are implemented ethically and equitably, ultimately contributing to the achievement of SDG 4 by making quality education accessible to all learners globally.

 

Developed in collaboration with the European Commission project 

Here is your window to the global efforts in AI and Education. This dashboard is configurable and can be integrated into other systems to bring actionable technology into the systems of research institutions, governments and enterprises that are willing to make a difference.

 

 

Developed in collaboration with the European Commission project 

Avoiding data bias in AI systems is crucial to ensure fair, accurate, and equitable outcomes, preventing the reinforcement of existing inequalities and enabling more trustworthy and inclusive technologies. Here you will see a dashboard analysing the bias related to the data ingested in this observatory for SDG 4.

For analysis we use OECD AI Policy documents. Some of those documents are very large, and we split each document into smaller parts (so called “chunks”), which can contain multiple paragraphs. The reason for this is to prepare data for easier analysis with large language models, so called Retrieval-Augmented Generation (RAG). RAG is an advanced technique that combines retrieval-based methods with generative models to improve the performance of tasks such as question answering, text generation, and other natural language processing (NLP) applications. For each chunk then the sentiment is computed based on VADER (Valence Aware Dictionary and sEntiment Reasoner) methodology. Since VADER is known to have weak multilingual capabilities, all the documents were machine translated into English first.

While the results of this procedure are reliant not only upon the accuracy of the sentiment analysis tool, but also upon the accuracy of machine translation, it is important to stress that sentiment analysis is less sensitive to common machine translation problems than other usages, because sentiment analysis usually focuses on identifying the polarity (positive, negative, neutral) of a text rather than understanding its full semantic content. Also, sentiments in text are often expressed redundantly, which can help mitigate the impact of translation errors. As a result, minor translation errors that do not alter the overall sentiment and do not significantly impact the sentiment analysis is possible.

For the purpose of this analysis, they computed the average sentiment of (chunks of) AI policy documents for each country. We are presenting the visualisation of average sentiment of countries’ AI policy documents on the map. Since AI policy documents are mostly documents of legal nature (acts, policies, regulatory and governance frameworks), the sentiment should be mostly neutral, however, the analysis shows that there are country differences.

VADER computes positive, negative and neutral sentiment. Each of those values are between 0 and 1. The score indicates the proportion of text that is considered positive, negative and neutral. The sum of negative, positive, and neutral sentiment scores always equals 1, however in practice the sum of three sentiment scores can sometimes slightly exceed or fall below 1 due to floating-point precision errors or rounding issues that occur during computation.

Developed in collaboration with the European Commission project