Researchers increasingly use artificial intelligence to construct measures of social, organizational, and occupational characteristics that are absent from conventional surveys. We propose AICOME, AI COntextual MEasurement, a framework for evaluating whether AI-derived respondent-level measures can recover individual and group-level effects in contextual models. The key idea is that an AI measure constructed at the respondent level can be used to derive its group-level aggregate and its individual deviation, allowing researchers to estimate both between-group and within-group associations rather than treating AI measurement as response prediction alone. We validate the framework using the 2022 China Family Panel Studies (CFPS), where occupations provide the empirical grouping structure and several job-related survey variables provide validation benchmarks. For computer use, foreign-language use, weekly hours, and management responsibilities, we compare survey measures with AI-derived measures in response-level, model-level, contextual, and boundary-condition validations. The results show that AI contextual measurement can recover much of the contextual-model information contained in observed survey variables when rich respondent and job characteristics are available. Weekly hours provides the strongest validation case, with AI-derived measures reproducing the large negative between- and within-occupation associations with satisfaction observed in CFPS. The framework also identifies clear boundary conditions: performance deteriorates when information is restricted to occupation and basic demographics, and recovery is weaker when several related concepts are treated as simultaneously unobserved. The findings suggest that AICOME is most useful for recovering a limited number of theoretically important constructs from rich existing datasets.
Synthetic personas based on large language models (LLMs) are increasingly proposed as substitutes for human survey respondents, yet systematic validation outside English-speaking contexts remains scarce. This secondary-data study evaluates how well a Korean synthetic persona panel (NVIDIA Nemotron-Personas-Korea), conditioned into Gemini 3.5 Flash (primary) and EXAONE (comparison), reproduces digital and AI service-use distributions from the KISDI Korea Media Panel Survey. Sex-and-age-stratified panels of about 8,000 personas per model answered the survey's own items - eight service-use indicators and eight innovativeness and acceptance constructs - and were compared against weighted survey estimates. The overall mean absolute error (MAE; RQ1) was 15-19 percentage points (pp), with binary item-mean correlations of 0.69-0.90. Segment error (RQ2) across five demographic axes was 15-19 pp, with between-group gaps up to 52.4/36.2 pp (Gemini/EXAONE). Errors followed model-specific signatures: an age stereotype with low anchoring (Gemini) versus an acquiescence-consistent level bias (EXAONE). Reference-year analysis was consistent with temporal misalignment driving most generative-AI overestimation, whereas short-form underestimation was framing-sensitive. Holdout calibration on 30% of the real data (RQ3) roughly halved sex-by-age cell MAE (18.9->8.6, 15.9->6.7 pp) - yet direct estimation from the same real subsample was far more accurate (3.6 pp), and the correction did not transfer across time. The calibrated panel retained an advantage only under extremely scarce real data (about 100 responses) and, for one model, for unobserved segments. Persona-narrative conditioning beat demographic-only conditioning, but neither surpassed simple real-data baselines. Synthetic panels are thus not survey substitutes; their value is diagnostic, with operational use confined to settings lacking real data.