With the advent of cyber‐social learning, it may be possible to overcome the limitations of statistical survey psychometrics and its associated methods: A multiliteracies perspective

Abstract Green and Giblin's ‘systematic review’ of multiliteracies in the December 2025 issue of this journal concludes: there is ‘no evidence’ of impact. This paper replies with a three‐layer analysis of that claim and the evidentiary regime behind it. Layer 1 assesses the review on its own terms, systematic review protocols and (quasi)experimental control/comparison designs, and finds fundamental methodological failures and a mismatch between multiliteracies and standardized literacy outcomes. Layer 2 locates the deeper problem in statistical survey psychometrics, arguing that tests, latent‐trait inference and normalized scoring are reductive, costly and increasingly anachronistic, with consequential harms for learning and equity. Layer 3 advances an alternative paradigm, cyber‐social learning research, enabled by integrated Contextual and Generative AI. Using the CyberScholar.ai environment as an illustration, we show how AI can analyse multimodal knowledge artefacts and learning‐process traces at scale, deliver rich feedback that transposes quality into quantity and reframe assessment and research as transparent, continuous and longitudinal evidence trails.