Are K-12 data systems ready for AI?

Here are some steps districts need to take before taking on AI

As educators who love technology, we can barely contain our enthusiasm for the potential applications of artificial intelligence (AI). But AI requires massive amounts of data, so before jumping on the AI bandwagon we need to:

  • reflect on the kinds of data that would make teaching more effective and improve learning outcomes;
  • consider the systems that will allow us to collect and manage the data; and
  • create processes to share and analyze the data.

Most districts do not yet have the foundation to make the leap to AI (other than what is already embedded in the apps and programs they’re currently using). Schools still exhibit a lack of maturity around data collection that should make us cautious about AI. There are also algorithmic bias and equity issues that need to be resolved before we move to wide-scale AI adoption. For most districts, spending money on AI over the next three to five years would be money down the drain. The ecosystems to support AI implementation are simply not yet in place in most schools and districts.

5 essential questions to test your district’s AI readiness

Before moving to AI, districts need to systematically build their digital landscape to get the full benefit of the technology they’re already using. Let’s begin with some basic questions:

  1. What kinds of data can help us make decisions to improve learning outcomes?
  2. Which programs can help us collect valid data and manage it safely?
  3. Is “adaptive” content always beneficial or is it sometimes more important to let teachers or students decide what comes next?
  4. What kind of feedback is the most valuable for student growth?
  5. When is an intervention a positive action and when does it eliminate constructive struggle, which is at the heart of deeper learning?

(Next page: How to evaluate your district’s data readiness for AI)

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