This study examines the conditions under which generative AI (GenAI) can support the identification and rating of perceptual indicators related to technology adoption in open-ended teacher discourse. Grounded in the Technology Acceptance Model (TAM), the study conceptualizes three levels of inferential demand: direct-evidence, synthesized-evidence, and TAM construct interpretation. Data from 174 K-12 teachers were analyzed through a human-AI comparative design using weighted Cohen’s kappa, before and after a structured training process. Findings indicate that agreement between GenAI and human raters varies systematically with inferential demand, with stronger alignment for direct-evidence indicators and more limited reliability & validity for higher-level, theory-driven constructs. Training improved alignment primarily for lower- and mid-level variables, while highlighting the role of “Insufficient Information” as a meaningful analytical outcome. The study introduces the DIRECT framework, which links inferential demand to the distribution of human-AI roles, offering a conceptual and practical guide for integrating GenAI into identification and rating of perceptual indicators.