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This page is about the intersection of education and machine learning in the context of climate change. For an overview of climate change education, please see the Wikipedia page on this topic.

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.

Machine Learning Application Areas[edit | edit source]

  • Creating adaptable educational tools: by using AI and ML techniques to adapt to learner behavior, enabling more powerful tools with less attrition rates.
  • Improving climate education: by improving existing existing climate education tools or by creating new ones using AI and ML.

Background Readings[edit | edit source]

Primers[edit | edit source]

  • 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[edit | edit source]

  • Climate Change Education: a course that presents the tools to teach climate change in a "positive, engaging and participatory way", curated by the Banco Interamericano de Desarollo.

Conferences, Journals, and Professional Organizations[edit | edit source]

Major journals[edit | edit source]

Major societies and organizations[edit | edit source]

Libraries and Tools[edit | edit source]

Data[edit | edit source]

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:

References[edit | edit source]

  1. Nkambou, Roger; Bourdeau, Jacqueline; Mizoguchi, Riichiro, eds. (2010). "Advances in Intelligent Tutoring Systems". Studies in Computational Intelligence. doi:10.1007/978-3-642-14363-2. ISSN 1860-949X.
  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)
  3. UNESCO (2015). "Not Just Hot Air: Putting Climate Change Education Into Practice". UNESCO Report.