2026-10-13T12:52:52Z https://www.scidb.cn/oai

10.57760/sciencedb.0103d 2026-08-31T10:17:14Z

2026-08-31 Corpus, Not Curriculum: Computational Corpus Planning in National AI Strategies doi:10.57760/sciencedb.0103d en National artificial intelligence strategies have proliferated rapidly, and many now make explicit commitments about language. This article asks a more basic question: what kind of object does language become in these documents? We screened all 193 United Nations member states and analysed 39 national AI strategies published between July 2024 and July 2026 using two independent coders. The contrast is striking. Language functions as an object of policy in 71.8 per cent of the documents, whereas foreign language education appears in only 7.7 per cent. Language, then, is not missing from AI policy. It appears in a particular register: as corpus, model and infrastructure; something to be assembled, trained and secured, rather than as a relationship between teachers and learners. We describe this as a distinct phase of language planning, computational corpus planning, in which the training dataset assumes the role once paradigmatically occupied by the dictionary and adequacy is defined positionally against other languages. A five-part typology of justification is developed and mapped across regions. Protection commitments were concentrated in Europe (8 of 12 cases), with only one Asian strategy employing a protection rationale, while linguistic-cultural sovereignty framing co-occurred with a language commitment in all fourteen cases in which it was coded. Where foreign language does surface, it is not treated as a competence to be developed but as friction to be engineered away: one strategy states that natural language processing “can compensate for the lack of literacy or foreign language skills.” We argue that the invisibility of language education in AI policy is therefore not simply an omission, but a consequence of how these documents constitute language in the first place. Inter-rater reliability is reported for all fields (κ = .791 for language-policy presence and .727 for foreign-language content). language policy · corpus planning · artificial intelligence · language technology · digital language death · comparative policy analysis · language ideology; AI strategies; language policy; computational corpus planning Bora Başaran PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank