When the Target Domain Changes: AI-Mediated Construct Drift in High-Stakes English Language AssessmenW
High-stakes English proficiency tests treat standardized, unaided performance as evidence for score interpretations about academic English proficiency. This interpretation remains meaningful, but as target language use domains increasingly involve generative AI, the extrapolation from unaided test performance to academic communicative readiness becomes less self-evident. This conceptual validity argument reframes AI as a score-interpretation problem in high-stakes language testing, not only an operational issue of scoring, feedback, security, or misconduct. Synthesizing current literature in three uneven layers, the paper shows that most work treats AI as assessment infrastructure, while ...
| Authors | Yi Gui |
|---|---|
| Primary category | cs.CL |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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