AI tools should be judged by the work they will actually do

Definition

AI systems should be tested against the local tasks, workflows, risks, and judgment calls they will actually handle, not only against generic benchmarks or vendor claims.

Current synthesis

This Big Idea gathers evidence from these Claims: ai-model-selection-requires-task-specific-evaluation.

This idea gathers sources arguing that model choice and AI adoption require situated tests tied to real work and expert review. AI tools should be tested on the real tasks they will be used for Failed AI pilots are useful evidence

Articles

• The Mythos Dilemma

• Giving Your AI a Job Interview

• AI Study Modes

• Brookings’ AI in K-12 Report: Benefits Remain Theoretical, Harms Are Already Here

• Why Your AI Strategy Needs Negative Results

• AI Sycophancy Is Not Always Harmful

• How to Model Effective AI Use in Classrooms

• What Happened When We Taught AI Literacy Like Writing

• The AI Revolution Looks Like Homework

• If Testing Companies Use AI to Grade

• What Happened When I Asked an AI Agent to Grade the Transcript

• The Car Wash Problem

• When AI Says This Quote Is Accurate

• Teachers’ AI Literacy and Agency in AI Integration: A Qualitative Study of Teachers in Delhi Private Schools

• A New Direction for Students in an AI World: Prosper, Prepare, Protect

• The Box and the Module

• What Is the Matter with Grading in the Age of AI?

• Claude Dispatch and the Power of Interfaces

• What Does “Investigate the Evidence” Mean?

Linked claims

• AI tools should be tested on the real tasks they will be used for

• Adult AI productivity gains do not automatically justify the same use for students

• Failed AI pilots are useful evidence

• AI grading systems need transparency, validation, and bias checks

• AI can make a poorly framed problem worse

• AI cannot be trusted to verify exact quotes by itself

• AI grading and feedback systems need validation before schools trust them

• The interface can limit how useful an AI tool really is

Relationship to linked claim

The linked claim AI tools should be tested on the real tasks they will be used for is the operational version of this big idea: the big idea names the principle, and the claim turns it into a procurement, pilot, and evaluation habit.

Open questions

• How should this idea be translated into concrete classroom routines, policies, or professional learning?