Measuring Secondary School Students’ Verification Capability in GenAI-Supported Learning: Development and Application of the α − v − M Framework

Generative Artificial Intelligence (GenAI) provides adolescents with fluent and immediate support, but unverified use may encourage cognitive offloading and passive reliance. This study conceptualizes verification capability as a cognitive defense mechanism through which learners inspect, question, correct, compare, or reconstruct GenAI outputs before accepting them. Using an exploratory sequential mixed-methods design, we first conducted deductive qualitative coding of 762 authentic Human–AI collaborative teaching cases. Among 242 valid GenAI-supported cases, 98.8% showed no documented verification of AI-generated outputs, indicating a severe observable verification deficit. We then developed and validated the α − v − M framework, comprising AI Engagement, Verification Intensity, and Multi-model Cross-validation, through a scale study with 422 secondary school students. Psychometric validation provided generally supportive evidence for the scale structure, and Item Response Theory was used to evaluate item functioning and generate latent trait scores. Structural equation modeling showed that AI engagement was associated with human–AI collaborative quality through verification-related processes. Gaussian graphical modeling identified deep logical auditing as a central cognitive defense indicator. Latent profile analysis further revealed four learner profiles: Naïve Trusters, Superficial Checkers, Isolated Auditors, and Symbiotic Strategists. These findings inform GenAI-supported learning design by emphasizing cognitive friction, verification scaffolds, and profile-sensitive feedback.