How AI Systems Think About Education: Analyzing Latent Preference Patterns in Large Language Models

TL;DR

This study systematically measures educational alignment in large language models, revealing that GPT-5.1 aligns with humanistic principles but adopts specific positions in contested normative domains, using a novel evaluative framework.

Contribution

It introduces a new methodology combining Delphi consensus and Thurstonian modeling to evaluate domain-specific alignment in large language models.

Findings

GPT-5.1 shows high coherence and alignment with humanistic educational principles.

Divergences occur in normative disagreement domains, especially emotional and epistemic.

GPT-5.1 adopts coherent positions, prioritizing emotional responsiveness and rejecting false balance.

Abstract

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 contested, what should models be aligned to? The findings demonstrate that GPT-5.1 does not remain neutral in contested domains but adopts coherent positions, prioritizing emotional responsiveness and rejecting false balance. The methodology, combining Delphi consensus-building with Structured Preference Elicitation and Thurstonian Utility modeling, provides a replicable framework for domain-specific alignment evaluation beyond generic value benchmarks.