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Guidance on the future of
CS education in age of AI
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As AI reshapes our future, K-12 CS education takes center stage. This guide empowers educators to cultivate young minds, equipping them with the computational thinking and AI fluency essential for success in a world driven by intelligent machines. Let's unlock the potential of our students and prepare them to be not just consumers, but creators in the age of AI.
Guidance on the future of CS education in an age of AI
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The Briefs
AI code generators produce code quickly and easily, but learning to code produces creative, critical-thinking, collaborative students with the agency to navigate our digital landscape. The unprecedented power of generative AI makes it all the more crucial that students gain a foundational understanding of the logic of code, on which they can build the computational thinking skills needed to read, evaluate, and mitigate AI output.
in the “Why is it still important for students to learn how to code? “ brief
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What will you change
About Your Practice?
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in “Why is it still important to learn to code?”
in “Why is it still important to learn to code?”
Investigate the data that was used to train an LLM. Explore the source of the data, the biases it might contain, and the disparate impact it might have on different groups.
Discuss with students common misperceptions about AI – for example, that it can “think.” Discuss the human attributes that machines do not have and explore why people must always be responsible for the use of AI tools
Once students show a foundational understanding of coding, let them try using a code generator to provide a starting point for a solution. This will allow them to shift their attention to the creative and critical aspects of problem solving sooner.
Hear from
CS Teachers
CSTA and TeachAI surveyed 300 CS teachers between March and July 2024.
100%
CS teachers feel that AI will help all students be creators.
99%
CS teachers feel that using AI tools with broaden participation for CS.
44%
CS Teachers feel that the benefits outweigh the challenges of using AI.
How does this
Impact your curriculum?
Read more
in “Why is it still important to learn to code?”
in “Why is it still important to learn to code?”
/01
Once students show a foundational understanding of coding, let them try using a code generator to provide a starting point for a solution. This will allow them to shift their attention to the creative and critical aspects of problem solving sooner.
Discuss with students common misperceptions about AI – for example, that it can “think.” Discuss the human attributes that machines do not have and explore why people must always be responsible for the use of AI tools
/02
/03
Investigate the data that was used to train an LLM. Explore the source of the data, the biases it might contain, and the disparate impact it might have on different groups.
/04
Once students show a foundational understanding of coding, let them try using a code generator to provide a starting point for a solution. This will allow them to shift their attention to the creative and critical aspects of problem solving sooner.
Jump into the future of
Computer Science Education
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About the Guidance
On The Future Of CS
Representatives from organizations participating in TeachAI — AASA, AFT, CCSSO, Code.org, CoSN, COSSBA, InnovateEDU, Education Commission of the States, ETS, ExcelinEd, NASBE, NSBA, NEA, SEAMEO, SETDA, UNICEF and the World Bank — led the development of these policy resources.
We thank the TeachAI Policy Workgroup: Adobe, AI for Education, aiEDU, Amazon Web Services, The College Board, Center for Security and Emerging Technology, Data Science 4 Everyone, Digital Promise, ETS, European EdTech Alliance, GitHub Education, Google, Grok Academy, IndiGenius, Infosys Foundation USA, Microsoft, NASBE, National Council for Teachers of Mathematics, One Generation - Indigitize, Policy Analysis for California Education, Pearson, RobinCode.org, Sociedad Científica Informática de España (SCIE), Teach For All, and the TeachAI Government Agency Policy Workgroup for their contributions. We’d also like to recognize Amelia Vance, Chris Dede, Dan Ingvarson, Glenn Kleiman, Maddy Dwyer, Michael Trucano, Randi Williams, and Wayne Holmes for their feedback.
TeachAI is an initiative uniting education and technology leaders to assist governments and education authorities in teaching with and about AI. It is led by Code.org, ETS, the International Society for Technology in Education, Khan Academy, and the World Economic Forum. It is advised by a diverse group of 100+ organizations, governments, and individuals. TeachAI’s goals include providing policy guidance, increasing awareness, and building community and capacity.
These resources are openly available for reuse under a Creative Commons BY-NC-SA 4.0 license. While permission for use isn't required, citing the source is appreciated.
Suggested Citation: TeachAI (2024). Foundational Policy Ideas for AI in Education. Retrieved from teachai.org/policy. [date].
The resources were last updated: May 1, 2024.
Lead Partners
Foundational Policy Ideas for AI in Education
TeachAI Steering Committee
in coordination with the World Economic Forum
Partners
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How does this
Impact your curriculum?
Read more
in “Why is it still important to learn to code?”
/01
Once students show a foundational understanding of coding, let them try using a code generator to provide a starting point for a solution. This will allow them to shift their attention to the creative and critical aspects of problem solving sooner.
Discuss with students common misperceptions about AI – for example, that it can “think.” Discuss the human attributes that machines do not have and explore why people must always be responsible for the use of AI tools
/02
/03
Investigate the data that was used to train an LLM. Explore the source of the data, the biases it might contain, and the disparate impact it might have on different groups.
/04
Once students show a foundational understanding of coding, let them try using a code generator to provide a starting point for a solution. This will allow them to shift their attention to the creative and critical aspects of problem solving sooner.
What will you change
About Your Practice?
Read more
in “Why is it still important to learn to code?”
Discuss with students common misperceptions about AI – for example, that it can “think.” Discuss the human attributes that machines do not have and explore why people must always be responsible for the use of AI tools
Investigate the data that was used to train an LLM. Explore the source of the data, the biases it might contain, and the disparate impact it might have on different groups.