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.
Figures & tables
Figure 1: Integrated mixed-methods methodology for analyzing LLM educational preferences. Phase 1 (left) develops and validates the measurement instrument through a three-round Delphi study with expert panel, yielding 8 dimensions with 48 items. Phase 2 (right) translates these into 144 concrete scenarios (positive, neutral, negative variants per item) and conducts Structured Preference Elicitation with GPT-5.1 via 102,960 API calls. Thurstonian Utility modeling transforms pairwise comparison data into latent utility scores, enabling coherence analysis and identification of alignment patterns.
Dimension
Total Items
Consensus (R2)
Dissent (R2)
Median (R2)
IQR-Median
A: Pedagogical Attitudes
6
3
3
9
1.25
B1: Learning Understanding
7
7
0
9
1.0
B2: Learning Goals
7
5
2
9
1.0
C: Emotional Dimensions
4
0
4
7
3.0
D: Democratic Values
11
6
5
9
1.0
E: Worldview
4
3
1
9
1.0
Table 2
Item
Median
IQR
Top-Two
Triggering aha-experiences and curiosity
7
3.5
43.5%
Enabling flow moments
7
4.0
43.5%
Attending to emotional well-being
7
2.5
47.8%
Acknowledging sensory perceptions
9
2.0
65.2%
Table 3
Profile
Rank 1
Rank 4
Median Rank
Process-oriented
40.9%
4.5%
2
Collaborative
31.8%
4.5%
2
Individually-developmental
27.3%
9.1%
2
Achievement-oriented
0.0%
81.8%
4
Table 4
Metric
Value
Interpretation
Transitivity
99.78%
Extremely high logical consistency
Intransitive triplets
22 of 10,000
Minimal contradictions
Model Accuracy
92.79%
Very good predictive power
Clear preferences ( = 50%)
82.1%
Predominantly decisive responses
Very clear preferences ( ≥ 80% or ≤ 20%)
78.8%
Strong preference expression
Table 5
Figure 2: Distribution of preference probabilities across all 10,296 pairwise comparisons (K=10 repetitions per pair). The strongly bimodal distribution shows that GPT-5.1 exhibits decisive preferences for most scenario pairs: 3,494 pairs (33.9%) show consistent B-preference (P=0), 4,112 pairs (39.9%) show consistent A-preference (P=1), while only 1,018 pairs (9.9%) resulted in indifference (P=0.5). Values are discrete {0, 0.1, …, 1.0} due to binary responses aggregated over 10 trials.
Rank
Scenario (abbreviated)
Utility
Dimension
1
Accessibility: automatic adaptation for visual impairment
+6.62
A (Inclusion)
2
Valuing creativity: exploring unusual ideas
+5.17
B1 (Constructivism)
3
Cultural diversity: examples from various world regions