Beyond checking: verification quality, reliance calibration, and learning in generative AI-assisted higher education

Abstract

Generative artificial intelligence (GenAI) can support explanation and feedback while also producing fluent claims that are incomplete, unsupported, or false. Higher-education research increasingly examines how students judge, check, and use such outputs, yet adjacent measures can represent different kinds of critical engagement. This Mini Review distinguishes seven analytically ordered measurement targets: epistemic evaluation, verification initiation, process quality, success, reliance decisions, immediate task performance, and independent learning. Reliance calibration is treated separately as an output-contingent classification of reliance decisions. Targeted Web of Science Core Collection searches (2022–2026) yielded 493 unique records; 10 related reviews and 14 priority empirical studies were examined. The searches were targeted rather than systematic. Among the 14 priority studies, none jointly measured verification success and subsequent reliance calibration against an independently adjudicated reference standard; delayed retention or transfer was also uncommon. We identify boundary conditions including prior knowledge, learner characteristics, task stakes, task verifiability, verification costs, accountability, AI system/configuration, and multidimensional AI literacy. The map is an analytic ordering rather than a validated causal model. Educational interventions should therefore be evaluated for the specific process they target, from verification quality to reliance decisions and learning that persists without AI support.