Assessment Validity in AI Education

Assessment validity — whether assessments measure what they claim to measure. AI in education raises fundamental validity questions: do AI-graded assessments assess student learning or AI prompting skill? Does AI use invalidate traditional assessment assumptions?

Validity challenges

Construct validity: When students use AI on assessments, does the score reflect student knowledge or AI capability? Performance vs. learning research addresses this directly.

Consequential validity: Do AI-mediated assessments have fair consequences? Language bias studies show that AI scoring can disadvantage non-native speakers.

Authentic assessment: Authentic Assessment and the AI Assessment Scale propose validity-preserving assessment redesigns.

Confidence and calibration: Confidence-aware systems improve validity by flagging uncertain assessments.

Redesign over detection

The wiki argues that maintaining assessment validity requires redesigning assessments for AI-capable students, not detecting AI use. Beyond detection approaches and Assessment represent validity-forward thinking.