Conditional validity in LLM-mediated L2 assessment: an argument-based systematic review and meta-analysis

Introduction Large language models (LLMs) are increasingly used for scoring and feedback in second-language (L2) assessment, yet the validity of the resulting interpretations remains contested. This review evaluated when LLM-mediated assessment is psychometrically and educationally defensible using an argument-based validity framework focused on construct representation, reliability and reproducibility, fairness, and washback. Methods Following PRISMA 2020, we systematically searched nine databases and repositories for empirical studies published from January 2022 to December 2025. Fifty-two studies met the inclusion criteria. Quantitative synthesis used REML random-effects models where effects were sufficiently comparable; Pearson correlations, rank correlations, ICC/κ/QWK, and other psychometric indices were otherwise retained on metric-appropriate scales. Washback outcomes were synthesized using Hedges' g. Results Source-level auditing showed that directly comparable Pearson evidence was sparse. Three source-verified L2 studies yielded moderate human-LLM score correspondence ( r = 0.66, 95% CI [0.53, 0.75]) with substantial heterogeneity ( I 2 = 70.8%). Evidence was strongest in rubric-guided, structurally constrained, and human-supervised contexts, whereas construct representation, fairness, operational reproducibility, and generalizability remained underdeveloped. Across 14 studies, LLM-mediated formative feedback produced a small-to-moderate positive effect on revision quality and short-term writing outcomes ( g = 0.42, 95% CI [0.27, 0.57]), although heterogeneity was substantial ( I 2 = 69.8%) and concerns about cognitive offloading, learner agency, and reproducibility persisted. Discussion The findings support a context-dependent interpretation of validity rather than a universal claim about LLM assessment capability. Current evidence supports carefully bounded formative and human-supervised applications but does not justify autonomous high-stakes scoring or broad claims of construct validity, fairness, and generalizability.