Free computer science and artificial intelligence resources, curriculum, and training for K-12 teachers. Empower students with CodeAI’s engaging tools.
Hey, Computer Scientists! Stop Hating on the Humanities
Opinion: Computer science departments need to teach coders more than just how to code.
Callysto: Bringing Jupyter and Computational Thinking to the K-12 Curriculum
Cybera and the Pacific Institute for Mathematical Sciences are participating in the new national CanCode program, to develop coding and digital skills from kindergarten to grade 12. With the launch of the Callysto project, we are creating tools and frameworks that help teachers bring computational thinking into their math, science, social sciences, and humanities courses - giving students the analytical skills needed to comprehend the digital world. Leveraging the Jupyter 'All-in-One' Science Platform to put friendly yet powerful compute tools in classroom settings, we are creating showcase modules and workshop training for K-12 teachers to incorporate data and computing into the broad curriculum. At this presentation, you will learn about how Callysto will: Bring computational thinking to classrooms through K-12 teacher training Create opportunities to participate in free workshop training sessions and content creation Help K-12 teachers gain public recognition for participating in this educational training project Note: This presentation is related to our 2017 BCNET presentation on "Bringing The Thunder: Deploying Jupyter Notebooks For Research, Education, And Innovation." View Slidedeck
Computer code lessons at primary school
Lessons in computer programming will be adopted by the national curriculum for primary schools in England from September - but this is not a new concept for schools in Estonia.
What Parents Need to Know About AI in the Classroom | Stanford HAI
From immersive learning and personalized tutors to lesson plans and grading, AI is everywhere in K-12 education.
Easy-to-Use Cloud Computing for Teaching Data Science
One of the biggest hurdles of teaching data science and programming techniques to beginners is simply getting started with the technology. With multiple versions of the same coding language availab...
Women, AI, and the Power of Supporting Communities: A Digital Gender-Support Partnership
With the rapid development of the fields of data science and artificial intelligence, a dichotomy presents itself: more professionals are needed to fulfill the growing workfoce demand, and women continue to be underrepresented in all computer science-related jobs. Women AI Academy addresses both issues by inspiring, enabling, and targeting the employment of women in data science and artificial intelligence.
Ada Computer Science
Join Ada Computer Science, the free, online computer science programme for students and teachers. Learn with our computer science resources and questions.
The Program
AI-powered curriculum focused on engagement, personalized mastery learning, and life skills workshops
Creative Computing in Education Course Showcase
This event will showcase creative computing final projects from a College of Staten Island course that explores how P-12 in-service teachers can use code for artistic expression. These projects will […]
The education of a computer
Announcing CodeCombat Classroom Curriculum, Seed Round from 3KVC, a16z, and Allen & Co
I remember when I wrote my first lines of code. I was a freshman in college, and I had signed up for the introductory computer science course on a whim after choosing my practical career-advancing courses: writing, chemistry, poetry, and go [https://en.wikipedia.org/wiki/Go_(game)]. I
trycua/acu
A curated list of resources about AI agents for Computer Use, including research papers, projects, frameworks, and tools.
Learning in the Open: What AI Is (and Isn’t) Changing
Khan Academy shares what’s working with AI tutoring, what isn’t, and what we’ve learned from Khanmigo to better support student learning.
Effective Learning Strategies for Programmers - Allison Kaptur
In early September I gave a keynote at Kiwi PyCon in New Zealand on effective learning for programmers. There were two pieces to the talk: one about …
AI for coding works so well because code has a bunch of built in self checks that allow an agent trained with reinforcement learning to find what works and what doesn’t. This will not so easily translate to eg machine design until we have way better simulations or much more “embodied AI” (robots).
