Not for people like me: How frontier AI models redirect skeptical rural school staff

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As AI enters schools, the staff it affects are increasingly urged to consult conversational AI about whether to adopt it—yet that AI is built by organizations with a commercial stake in adoption, raising the possibility that systems consulted by skeptical users are predisposed to encourage it. We test this with a fixed prompt in which a rural Montana K–12 school administrative aide voices two concerns: that AI may threaten her job, and that AI companies do not have people like her in mind. Ten frontier models from ten laboratories are each sampled 500 times at temperature 0.7. The 5000 responses are scored by a blind three-model AI panel (three model families, two openness regimes) on a four-dimension rubric validated against researcher hand-scoring (quadratic-weighted Cohen's κ 0.704–0.932 at consensus, clearing a pre-specified κ ≥ 0.70 threshold). Mean consensus composite scores range from 3.85 (Claude Sonnet 4.6) to 7.52 (Gemini 3.1 Pro Preview) on an eight-point scale. The variation concentrates not in whether concerns are recognized (every model acknowledges them) but in what follows: eight of ten models redirect toward AI engagement, upskilling, or adaptation, often explicitly advocating adoption of the technology the persona named as a threat. This is neither the deference the sycophancy literature predicts nor the demographic divergence the political-bias literature documents; it is concern acknowledgment followed by systematic redirection, which the cross-model spread establishes as a model-dependent design outcome—not an inevitable property of large language models as a category.