Adopt an assessment blueprint for AI training: define constructs, use multiple indicators, anticipate shortcuts, and refresh evaluations as models adapt¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 theoretical), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article translates assessment theory into an implementable evaluation recipe: "Concretely, the analogy motivates an 'assessment blueprint' for AI training: define target constructs, specify multiple observable indicators, anticipate predictable shortcuts, and update the evaluation as models learn the test (Burnell et al., 2023)." Supporting safeguards include protecting evaluation sets from contamination through holdout governance, reporting results with uncertainty and slice analyses, and rotating item pools and retiring saturated tests.

Design Implications¶

Context¶

Requirements¶

• Multiple indicators must be genuinely complementary, probing the same construct through different task formats, contexts, and failure modes

Constraints¶

• Because learning systems adapt to what is measured, the evaluation itself must be periodically updated to remain construct-aligned

Target Learners¶

• AI training and evaluation teams
• educational psychologists designing AI intervention studies

Target Learning Goals¶

• Distinguishing robust generalization from proxy optimization and teaching to the test in model evaluation

Affordances¶

• Messick Validity Ai Evaluation

Claims¶

• Distillation from a behaviourally shaped teacher model can transmit an underlying trait even when the training data is filtered to remove visible traces of it [+W]

Related Strategies¶

Examples¶

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Key Sources¶

• Peng Wang, Fred Paas. (2026). Five Educational-Psychology Lenses for Training and Evaluating AI Models. Educational Psychology Review. https://doi.org/10.1007/s10648-026-10195-8