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U.S. Department of Education Releases Guidance on Responsible Use of Education Technology in the Classroom
U.S. Department of Education Releases Guidance on Responsible Use of Education Technology in the Classroom
Today, the U.S. Department of Education (the Department) released new guidance to help states, school districts, educators, families, and education technology providers make informed decisions about the responsible use of technology in classrooms.
U.S. Department of Education
·ed.gov·
U.S. Department of Education Releases Guidance on Responsible Use of Education Technology in the Classroom
Andrew Ng on X: "The AI Engineering Skills Map" / X
Andrew Ng on X: "The AI Engineering Skills Map" / X
everyone with the skills to take advantage of this shift has numerous exciting project and job opportunities
I have been working with my team to synthesize a map of AI engineering skills in order to help (i) developers prioritize what to learn, and (ii) employers hire skilled developers.
Building and deploying AI applications Software engineering fundamentals Using coding agents Shaping the build
building and deploying AI applications understand the building blocks of AI (such as LLMs, context engineering, RAG, agentic workflows, machine learning and deep learning) and, importantly, how to use statistical techniques to measure, steer, and govern AI systems so that they behave more predictably
drive disciplined evals and error analysis loops.
deeply understand how software works, you can build much more effectively
tradeoffs between cost, scalability, reliability, speed, and more. Security and privacy add further complexity.
better decisions in choosing your software stack, designing system architecture, designing your data store, testing
steering coding agents using the precise language of software engineering.
know what context to give their coding agent.
have a good mental model for how agents work. You understand their limitations and how to work around them, and are able to quickly steer them — knowing how much to intervene and how much to leave them alone — to build robust software without wasting excessive time or tokens.
manage a coding agent’s context, make tradeoffs between planning and execution, and help the agent autonomously close loops by providing verifiers or evals
work with a clear spec (and when not to bother doing so), orchestrate multiple agents that work together, and avoid pitfalls like risk an agent messing up your production database
routines to keep trying new tools and evolve your workflows as best practices change.
our work as engineers is shifting toward deciding what should be in the spec
having product sense and understanding business context and customer goals
take on greater ownership and agency
identify interesting problems and opportunities, and execute to take advantage of them in responsible ways
must all keep learning and evolving our skills to adopt emerging best practices.
·x.com·
Andrew Ng on X: "The AI Engineering Skills Map" / X