The 4D Framework for pK-12 educators — Transcript
Full transcript
- 0:01[snorts]
- 0:02[music]
- 0:07>> We want you to build lasting AI fluency.
- 0:10That means giving you the tools to use
- 0:12AI efficiently, ethically, and safely,
- 0:15no matter what challenges you might
- 0:16face. In this video, we'll walk through
- 0:18the 4D framework, four interconnected
- 0:21competencies that, when combined,
- 0:23transform how you work with AI. To help
- 0:26you apply this framework, we'll look at
- 0:28two modes of interaction with AI. The
- 0:30first mode is likely the one you're
- 0:32familiar with, how to engage with AI
- 0:35effectively on a day-to-day basis. This
- 0:38is the inner loop of description and
- 0:40discernment. You describe what you want
- 0:42AI to help you with, and then you
- 0:43discern if it meets your expectations.
- 0:46This is a critically important skill set
- 0:48when learning AI, but it's not
- 0:50sufficient on its own. In addition to
- 0:52everything that happens within your
- 0:53interactions with AI, there's also an
- 0:55outer loop of delegation and diligence.
- 0:58This is everything that happens in your
- 1:00workplace, community, and life around
- 1:02the use of AI. Should you be using AI at
- 1:04all? And if you do, what are your
- 1:07ethical responsibilities to ensure
- 1:08you're proceeding intentionally and
- 1:10responsibly? We'll be doing a brief
- 1:12overview of all of this today, but if
- 1:14you'd like a deeper understanding, check
- 1:16out the AI Fluency Framework and
- 1:18Foundations course on Anthropic Academy.
- 1:21Let's begin with a close look at the
- 1:23inner loop of description and
- 1:25discernment. Again, this happens during
- 1:27your day-to-day interactions with AI.
- 1:31Description is how we refer to
- 1:32communicating effectively with AI
- 1:34systems. Good prompt matters, but so
- 1:37does everything around it. The context
- 1:39you provide, the examples you share, how
- 1:41you refine and redirect the
- 1:43conversation.
- 1:44It all starts with defining what you
- 1:46want, your outputs, format, audience,
- 1:48and style.
- 1:50Instead of write a summary of this
- 1:51report, try getting specific.
- 1:54Summarize this report for an
- 1:56eighth-grade audience, focusing on the
- 1:57three highest impact findings in under
- 1:59300 words.
- 2:01But you can also define how the AI
- 2:03approaches your request. Think of it
- 2:05like giving instructions to a
- 2:06collaborator specifying the steps, the
- 2:09order of operations, or the reasoning
- 2:11approach you want the AI to follow.
- 2:13And you can shape the AI's behavior
- 2:15during your collaboration as well. Do
- 2:17you need a critical reviewer who pushes
- 2:20back on weak arguments, a brainstorming
- 2:22partner who builds on every idea, a
- 2:24fact-checker who flags uncertainty? You
- 2:27can ask for all of it.
- 2:29In our research, we found that
- 2:30description skills are the ones people
- 2:32pick up most naturally. Things like
- 2:35specifying what you want, giving
- 2:36examples, and refining your requests.
- 2:39Most people are already doing some
- 2:40version of these, and that's a great
- 2:42starting point. As you iterate and
- 2:44collaborate with AI, it's likely you'll
- 2:46get more comfortable and effective at
- 2:48description.
- 2:50Discernment, the companion to
- 2:51description, asks you to thoughtfully
- 2:54and critically evaluate your AI
- 2:56collaborator. Start with the output
- 2:58itself. Are these statistics accurate?
- 3:01Does this language reflect how your
- 3:02audience actually talks about this
- 3:04topic? Then look at how AI got there.
- 3:07Did it consider all relevant factors?
- 3:10Importantly, discernment doesn't just
- 3:12mean accepting or rejecting AI outputs.
- 3:14It involves iteration to get where you
- 3:17want to go.
- 3:18And while most people describe what they
- 3:20want fairly naturally, discernment is
- 3:22much rarer. In our research, skills like
- 3:25questioning AI's reasoning, checking
- 3:27facts, and identifying missing context
- 3:29were among the least common behaviors we
- 3:31observed. And that's where there's room
- 3:33to grow.
- 3:34It can be an easy step to miss, but it's
- 3:36critically important. It's what drives
- 3:38true AI collaboration. That's why
- 3:41description and discernment work
- 3:43together in a loop. You describe what
- 3:46you need, evaluate what you get, and
- 3:48then refine your description based on
- 3:50that evaluation. It's like working with
- 3:52a human teammate. You build a shared
- 3:54understanding through conversation.
- 3:57Using AI well in day-to-day interactions
- 3:59is only one piece of the puzzle. It's
- 4:01critical to understand all the ethical
- 4:03and social implications of AI use in
- 4:05your context. This is where the
- 4:07delegation diligence loop comes in.
- 4:09Delegation is deciding what work should
- 4:11be done by humans, what work should be
- 4:12done by AI, and how to distribute the
- 4:14tasks between them.
- 4:16Good delegation starts with
- 4:18understanding the work itself. Before
- 4:20drafting an email, ask yourself, is this
- 4:22a routine status update where AI can
- 4:24handle a first draft, or is this a
- 4:26sensitive message to a family about
- 4:28their child's progress where tone and
- 4:30nuance need to come directly from you?
- 4:32You also need to understand your tools.
- 4:35If you're working with confidential
- 4:36data, for example, you'd want to
- 4:38consider the privacy and security
- 4:39features of the AI system you're using.
- 4:42When you understand both the work and
- 4:44the tools, you can thoughtfully split
- 4:45tasks to leverage the strengths of each.
- 4:48We pair delegation with diligence,
- 4:51taking responsibility for how you use
- 4:54AI.
- 4:55That means being thoughtful about which
- 4:57AI systems you use and how you interact
- 4:59with them. Maybe you use AI to draft a
- 5:01project proposal, but you're intentional
- 5:03about which sections it works on and
- 5:05which require your own expertise.
- 5:08It means being honest about AI's role in
- 5:10your work with the people who need to
- 5:12know.
- 5:13Your colleagues and stakeholders deserve
- 5:15to understand when and how AI has been
- 5:18involved. And it means taking
- 5:20responsibility for verifying and
- 5:22vouching for the outputs you use or
- 5:23share. You check every claim, confirm
- 5:26accuracy, and make sure any final
- 5:28deliverable generally represents your
- 5:30goals and standards. You're always
- 5:32accountable for the final result.
- 5:34Delegation and diligence work together
- 5:37as a loop. The thoughtful choices you
- 5:39make about what to delegate to AI must
- 5:42be matched by ongoing responsibility for
- 5:44how how use it. And your diligent
- 5:47practices inform smarter delegation
- 5:49decisions over time.
- 5:50The four D's come alive when you use
- 5:53them together, creating a new syllabus,
- 5:55use delegation to decide what AI handles
- 5:58versus what you bring yourself.
- 6:00Use strong description to guide the AI's
- 6:02work, apply discernment to evaluate the
- 6:05results, and practice diligence
- 6:07throughout by choosing appropriate
- 6:09tools, being transparent about AI's
- 6:11role, and taking responsibility for
- 6:13accuracy. This framework is about making
- 6:15you more effective at the work that
- 6:17matters. Work that requires human
- 6:19judgment, creativity, and deep
- 6:21understanding of your context.
- 6:23Throughout this course, you'll apply
- 6:24these competencies to real challenges,
- 6:27creating differentiated instructional
- 6:29materials, analyzing data while
- 6:31protecting student privacy, and building
- 6:33lesson plans. The skills you take away
- 6:35will serve you as AI tools continue to
- 6:37evolve. You can learn more about AI
- 6:40fluency in Anthropic Academy, and we'll
- 6:42continue to share our research on this
- 6:43topic on Anthropic's blog.
- 6:47>> [music]
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