@inproceedings{hoque-etal-2026-equal,
title = "{EQUAL}: Evaluating Pluralistic Alignment of {LLM}s Across Educational Perspectives",
author = "Hoque, MD Azizul and
Dras, Mark and
Naseem, Usman",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1909/",
pages = "43491--43515",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Models (LLMs) are increasingly deployed in education, powering tutoring systems, automated feedback, and curriculum support. Most alignment techniques optimize toward a single aggregated notion of human preference, a mismatch for education, where legitimate disagreement is the norm. Students, parents, teachers, and policymakers often hold deeply conflicting views on assessment, discipline, curriculum, and institutional authority, reflecting genuine value pluralism rather than noise. Pluralistic alignment, which seeks to represent and navigate this diversity, offers a principled path forward. Despite growing interest, no benchmark evaluates how well LLMs handle value-laden disagreement in educational contexts. To address this, we introduce EQUAL (Eliciting QUality sAmples of pLuralistic alignment in education), comprising 14,270 samples{---}5,824 value-laden scenarios and 8,446 multiple-choice questions, curated from six sources, including over two decades of Phi Delta Kappa polling data, via a lexical filtering pipeline and human validation. EQUAL supports evaluation across Overton, Steerable, and Distributional paradigms. We benchmark nine LLMs under four approaches and additionally evaluate three frontier reasoning-capable models. No single method consistently satisfies pluralistic objectives. These findings provide a foundation for future research on pluralistic alignment in educational AI."
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<abstract>Large Language Models (LLMs) are increasingly deployed in education, powering tutoring systems, automated feedback, and curriculum support. Most alignment techniques optimize toward a single aggregated notion of human preference, a mismatch for education, where legitimate disagreement is the norm. Students, parents, teachers, and policymakers often hold deeply conflicting views on assessment, discipline, curriculum, and institutional authority, reflecting genuine value pluralism rather than noise. Pluralistic alignment, which seeks to represent and navigate this diversity, offers a principled path forward. Despite growing interest, no benchmark evaluates how well LLMs handle value-laden disagreement in educational contexts. To address this, we introduce EQUAL (Eliciting QUality sAmples of pLuralistic alignment in education), comprising 14,270 samples—5,824 value-laden scenarios and 8,446 multiple-choice questions, curated from six sources, including over two decades of Phi Delta Kappa polling data, via a lexical filtering pipeline and human validation. EQUAL supports evaluation across Overton, Steerable, and Distributional paradigms. We benchmark nine LLMs under four approaches and additionally evaluate three frontier reasoning-capable models. No single method consistently satisfies pluralistic objectives. These findings provide a foundation for future research on pluralistic alignment in educational AI.</abstract>
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%0 Conference Proceedings
%T EQUAL: Evaluating Pluralistic Alignment of LLMs Across Educational Perspectives
%A Hoque, MD Azizul
%A Dras, Mark
%A Naseem, Usman
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hoque-etal-2026-equal
%X Large Language Models (LLMs) are increasingly deployed in education, powering tutoring systems, automated feedback, and curriculum support. Most alignment techniques optimize toward a single aggregated notion of human preference, a mismatch for education, where legitimate disagreement is the norm. Students, parents, teachers, and policymakers often hold deeply conflicting views on assessment, discipline, curriculum, and institutional authority, reflecting genuine value pluralism rather than noise. Pluralistic alignment, which seeks to represent and navigate this diversity, offers a principled path forward. Despite growing interest, no benchmark evaluates how well LLMs handle value-laden disagreement in educational contexts. To address this, we introduce EQUAL (Eliciting QUality sAmples of pLuralistic alignment in education), comprising 14,270 samples—5,824 value-laden scenarios and 8,446 multiple-choice questions, curated from six sources, including over two decades of Phi Delta Kappa polling data, via a lexical filtering pipeline and human validation. EQUAL supports evaluation across Overton, Steerable, and Distributional paradigms. We benchmark nine LLMs under four approaches and additionally evaluate three frontier reasoning-capable models. No single method consistently satisfies pluralistic objectives. These findings provide a foundation for future research on pluralistic alignment in educational AI.
%U https://aclanthology.org/2026.emnlp-main.1909/
%P 43491-43515
Markdown (Informal)
[EQUAL: Evaluating Pluralistic Alignment of LLMs Across Educational Perspectives](https://aclanthology.org/2026.emnlp-main.1909/) (Hoque et al., EMNLP 2026)
ACL