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        <title>Rewrites Are Not Lessons: Teachable Edits as an Output Specification for Multimodal Language Learning</title>
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    <abstract>Multimodal large language models are increasingly described as tutors for language learning: they take a handwritten page, a spoken utterance, or a slide and return fluent target-language text. We argue that this description confuses a correction engine with a teaching intervention. For systems claiming to teach through corrective feedback, we propose a teachable edit: localized on the learner’s production, typed by error category, conservative with respect to the learner’s interlanguage, and accompanied either by a faithful, level-appropriate explanation or by a recorded, calibrated abstention. Fluency of a rewritten string is the wrong success criterion. Existing explainable and pedagogically targeted grammatical error correction (GEC) work already supplies evidence words, error types, and grammar-item evaluation on text. What remains underspecified is a single record that also localizes into a multimodal artifact, scores conservatism and explanation-to-edit faithfulness, and requires calibrated abstention. We illustrate the specification on Chinese and Arabic examples, using the latter to show how language-specific error categories and right-to-left script affect localization while leaving the functional structure of the record unchanged. We state that record, compare it with EXPECT, EXCGEC, and JELV, and separate correction correctness from feedback quality and from learning outcomes. The correction, localization, and reference-based estimates of conservatism can be measured on existing benchmarks; the explanation, level, and abstention fields need a small annotation pass, which we specify and do not run.</abstract>
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%0 Conference Proceedings
%T Rewrites Are Not Lessons: Teachable Edits as an Output Specification for Multimodal Language Learning
%A Zaghouani, Wajdi
%Y Wang, Xiaoman
%Y Wang, Qianyu
%Y Liu, Zhengyuan
%Y Chen, Nancy F.
%Y Yuan, Zheng
%Y Lan, Yunshi
%Y Jiang, Jing
%S Proceedings of the 1st Workshop on Multi-modality and Multi-lingual Language Learning
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, Zhuhai, China
%@ 979-8-89176-449-1
%F zaghouani-2026-rewrites
%X Multimodal large language models are increasingly described as tutors for language learning: they take a handwritten page, a spoken utterance, or a slide and return fluent target-language text. We argue that this description confuses a correction engine with a teaching intervention. For systems claiming to teach through corrective feedback, we propose a teachable edit: localized on the learner’s production, typed by error category, conservative with respect to the learner’s interlanguage, and accompanied either by a faithful, level-appropriate explanation or by a recorded, calibrated abstention. Fluency of a rewritten string is the wrong success criterion. Existing explainable and pedagogically targeted grammatical error correction (GEC) work already supplies evidence words, error types, and grammar-item evaluation on text. What remains underspecified is a single record that also localizes into a multimodal artifact, scores conservatism and explanation-to-edit faithfulness, and requires calibrated abstention. We illustrate the specification on Chinese and Arabic examples, using the latter to show how language-specific error categories and right-to-left script affect localization while leaving the functional structure of the record unchanged. We state that record, compare it with EXPECT, EXCGEC, and JELV, and separate correction correctness from feedback quality and from learning outcomes. The correction, localization, and reference-based estimates of conservatism can be measured on existing benchmarks; the explanation, level, and abstention fields need a small annotation pass, which we specify and do not run.
%U https://aclanthology.org/2026.multilll-1.7/
%P 56-64

Markdown (Informal)

[Rewrites Are Not Lessons: Teachable Edits as an Output Specification for Multimodal Language Learning](https://aclanthology.org/2026.multilll-1.7/) (Zaghouani, Multi-LLL 2026)

• Rewrites Are Not Lessons: Teachable Edits as an Output Specification for Multimodal Language Learning (Zaghouani, Multi-LLL 2026)

ACL

• Wajdi Zaghouani. 2026. Rewrites Are Not Lessons: Teachable Edits as an Output Specification for Multimodal Language Learning. In Proceedings of the 1st Workshop on Multi-modality and Multi-lingual Language Learning, pages 56–64, Hengqin, Zhuhai, China. Association for Computational Linguistics.