How AI Systems Think About Education: Analyzing Latent Preference Patterns in Large Language Models
This paper presents the first systematic measurement of educational alignment in Large Language Models. Using a Delphi-validated instrument comprising 48 items across eight educational-theoretical dimensions, the study reveals that GPT-5.1 exhibits highly coherent preference patterns (99.78% transitivity; 92.79% model accuracy) that largely align with humanistic educational principles where expert consensus exists. Crucially, divergences from expert opinion occur precisely in domains of normative disagreement among human experts themselves, particularly emotional dimensions and epistemic normativity. This raises a fundamental question for alignment research: When human values are conteste...
| Authors | Daniel Autenrieth |
|---|---|
| Primary category | cs.CY |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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