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On the one hand, in addition to being universally beneficial, education can improve the resilience of communities to climate change, especially in developing countries. ML can help enable personalized and scalable tools for education. On the other, education can empower individuals to adopt more sustainable lifestyles. ML can help educate the public about climate change through conversational agents and adaptive learning techniques.

Background Readings


  • Advances In Intelligent Tutoring Systems (2010) [1]: the textbook on creating adaptable learning agents, with chapters dedicated to different approaches and theories. Available here.
  • Another 25 Years of AIED? Challenges and Opportunities for Intelligent Educational Technologies Of The Future (2016)[2]: a thorough analysis of the promise of Artificial Intelligence in education and the challenges that it entails. Available here.
  • Not Just Hot Air: Putting Climate Change Education Into Practice (2015)[3]: a primer prepared by the UNESCO about teaching climate change education to different populations of students.

Online courses and course materials


Major journals

Major societies and organizations

Libraries and tools


There are many hurdles in accessing data generated from educational settings, given the privacy issues that arise and the digital divide that exists in many countries,where learning is offline. There are nonetheless a few data sources that can be of interest:

Selected problems


  1. Advances in Intelligent Tutoring Systems. Springer Berlin Heidelberg. Retrieved 2020-08-28.
  2. Pinkwart, Niels (2016-06). "Another 25 Years of AIED? Challenges and Opportunities for Intelligent Educational Technologies of the Future". International Journal of Artificial Intelligence in Education. 26 (2): 771–783. doi:10.1007/s40593-016-0099-7. ISSN 1560-4292. Check date values in: |date= (help)
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