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    <abstract>Educational LLMs such as writing coaches, coding assistants, study companions, and AI tutors are widely marketed as student-centered, usually because they are “personalized” or because they raise performance on the assisted task. We argue that this is an unverified pedagogical claim: a system that helps students complete tasks is not necessarily a system that helps students learn. The distinction is between performance-centered assistance, which optimizes the immediate product, and student-centered learning, which preserves the learner’s cognitive work and validates capability the learner keeps without the system. A 2025 randomized trial shows the gap in practice: an unguarded tutor raised assisted performance while harming unassisted exam scores. We trace this to a units mismatch: “student-centered” is a learner-side claim, yet it is measured almost entirely with tutor-side proxies. We propose five auditable standards (S1–S5) that translate established learning-science principles into requirements for benchmark design, reward shaping, red-teaming, and deployment monitoring; a small illustrative process audit then shows both that the standards can be operationalized and why such audits cannot substitute for learner-side validation. Our position is that LLM-based educational systems should be judged not by answer quality, engagement, satisfaction, or assisted performance alone, but by whether they measurably support learners’ capacity to reason, self-regulate, transfer, and keep learning without the model.</abstract>
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%0 Conference Proceedings
%T Student-Centered AI Requires Auditable Evidence of Learning
%A Aofeng, Xu
%A Li, Sichao
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F aofeng-li-2026-student
%X Educational LLMs such as writing coaches, coding assistants, study companions, and AI tutors are widely marketed as student-centered, usually because they are “personalized” or because they raise performance on the assisted task. We argue that this is an unverified pedagogical claim: a system that helps students complete tasks is not necessarily a system that helps students learn. The distinction is between performance-centered assistance, which optimizes the immediate product, and student-centered learning, which preserves the learner’s cognitive work and validates capability the learner keeps without the system. A 2025 randomized trial shows the gap in practice: an unguarded tutor raised assisted performance while harming unassisted exam scores. We trace this to a units mismatch: “student-centered” is a learner-side claim, yet it is measured almost entirely with tutor-side proxies. We propose five auditable standards (S1–S5) that translate established learning-science principles into requirements for benchmark design, reward shaping, red-teaming, and deployment monitoring; a small illustrative process audit then shows both that the standards can be operationalized and why such audits cannot substitute for learner-side validation. Our position is that LLM-based educational systems should be judged not by answer quality, engagement, satisfaction, or assisted performance alone, but by whether they measurably support learners’ capacity to reason, self-regulate, transfer, and keep learning without the model.
%U https://aclanthology.org/2026.findings-aacl.130/
%P 2544-2560

Markdown (Informal)

[Student-Centered AI Requires Auditable Evidence of Learning](https://aclanthology.org/2026.findings-aacl.130/) (Aofeng & Li, Findings 2026)

• Student-Centered AI Requires Auditable Evidence of Learning (Aofeng & Li, Findings 2026)

ACL

• Xu Aofeng and Sichao Li. 2026. Student-Centered AI Requires Auditable Evidence of Learning. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 2544–2560, Hengqin, China. Association for Computational Linguistics.