The Thirsty Cloud: Tracking AI's Hidden Water Footprint
NGSS Performance Task · ngss
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Rubric
Name: Engaging in argument from evidence
Advanced: Meets all proficient criteria. Also evaluates the limits of the evidence. For example, the student explains that the table shows correlation, and that rainfall staying the same helps rule out one alternative cause, but other water users could still contribute. The student then proposes data that would test causation, such as hourly water use compared with hourly AI demand. Compares the scale of data center water use with the reservoir's total volume, or with residential use, to judge how large the impact is. Uses this quantitative reasoning to argue for or against approving a second data center, and connects the argument directly to the proposed monitoring method and design solution. When a classmate rebuts the argument, the student revises or qualifies the claim in response to new evidence and explains why the change was made.
Beginning: States a claim about the data center's effect on the reservoir (e.g., 'the data center uses up our water') but cites no specific values from the April–July table or sources. Does not connect rising temperature to cooling water use as a cause-and-effect mechanism. Uses no scale reasoning about per-question water multiplied by millions of daily queries. When classmates offer a different claim, repeats the original claim without referring to evidence.
Dimension: SEP
Developing: States a claim and cites at least one data pattern from the table (e.g., water use rose from 38 to 72 million liters as average high temperature rose from 21 to 35°C). Reasoning names only part of the mechanism, such as 'hot weather means more water,' without explaining that servers produce heat and cooling towers evaporate water to remove it. Attempts scale reasoning with a per-question estimate but does not multiply it to a daily or monthly total. Responds to one classmate's counterclaim by restating evidence instead of evaluating the classmate's evidence.
Proficient: Makes a claim about how chatbot use and summer heat together increase the data center's water draw and lower the reservoir. Supports the claim with at least two pieces of evidence: the matching April–July trends in the table, and information from a source the student has judged reliable, such as a sustainability report or local water data. Explains the cause-and-effect chain step by step: a query creates computing work, the work produces heat, cooling towers evaporate water to remove the heat, and the reservoir drops. Includes a scale calculation that converts an estimated water amount per query into a daily or monthly community-scale volume. In the proposal or rebuttal, identifies a specific weakness in a classmate's competing claim, such as the claim ignoring that rainfall stayed about the same, and cites evidence to counter it.
Procedure
Phenomenon
Task Scenario
Teacher Notes
Ngss Alignment
Driving Question
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