The six challenges to integrate artificial intelligence in education | Trends

The six challenges to integrate artificial intelligence in education | Trends

It has been the private sector that has led the development of artificial intelligence, especially in the US and China. The omnipresence of AI in all aspects of human activity, more and more governments are beginning to actively implement concrete responses to AI, according to a report released by ProFuturo, an education program launched in 2016 by Fundación Telefónica and Fundación Bancaria la Caixa, whose mission is to reduce the educational gap in the world by providing quality digital education to children in vulnerable environments in Latin America, Sub-Saharan Africa and Asia.

Some countries, such as France, Australia, Estonia, South Korea, China and the United States have implemented AI strategies at the national level. In all these answers, education is a complete element. However, in developing countries, these issues are not yet posed and are limited by structural obstacles (basic technological infrastructure, high profile human resources in the field of AI, etc.).

Educational systems must be reformed to ensure that students acquire the necessary skills for a future workplace prepared for AI. These reforms occur in all the educational subsectors and must be reoriented towards a permanent education that is re-considered on a regular and continuous basis. Given that these reforms are applied because of the gap that exists in the use capacities of the AI, there must also be greater dialogue and collaboration between the industry and the education sector.

  • Challenge 1 The first of all is to develop a comprehensive public policy on AI for development. The complexity of the technological conditions necessary to advance in this field requires the convergence of multiple factors and institutions. Public policies must work together at the local and international level to create an AI ecosystem for development.
  • Challenge 2 guarantee an inclusive and equitable use of AI in education. Less developed countries risk new social and technological divisions with the development of AI. It is necessary to face some important obstacles, such as the basic technological infrastructure, to establish the basic conditions for the implementation of new strategies that use AI to improve learning.
  • Challenge 3 We must prepare teachers for an AI-driven education, while preparing AI to understand education, although it is a shared responsibility: teachers must learn new digital skills to use AI in a meaningful way and pedagogical, and AI developers must learn how teachers work and create sustainable solutions in real environments.
  • Challenge 4it consists of developing inclusive and quality data systems. If we address the datafication of education, the quality of the data should be our main concern. It is essential to develop state capacities to improve the systematization and collection of data. Advances in AI must be an opportunity for data to become important in the administration of education systems.
  • Challenge 5 It is necessary to make the research on AI in education meaningful. Although we can expect that research on educational AI becomes more important in the coming years, it is necessary to remember the difficulties that the educational sector has faced in order to take stock of educational research in a significant way at a practical and legislative level.
  • Challenge 6 This challenge embodies ethics and transparency in the collection, use and dissemination of data. The AI ​​raises many ethical concerns about access to the education system, recommendations for individual students, concentration of personal data, responsibility, impact on work, privacy of data and ownership of the data that is added to the algorithms. Therefore, the regulation of AI requires a public debate on ethics, responsibility, transparency and security.


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