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| 1 | Elfrieda Hiebert | -- | 943 | 2026-09-17 08:07:27 |
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Hiebert, E.H. Which Words to Teach: Word Selection in the Age of Large Language Models. Encyclopedia. Available online: https://encyclopedia.pub/entry/60091 (accessed on 23 September 2026).
Hiebert EH. Which Words to Teach: Word Selection in the Age of Large Language Models. Encyclopedia. Available at: https://encyclopedia.pub/entry/60091. Accessed September 23, 2026.
Hiebert, Elfrieda H.. "Which Words to Teach: Word Selection in the Age of Large Language Models" Encyclopedia, https://encyclopedia.pub/entry/60091 (accessed September 23, 2026).
Hiebert, E.H. (2026, September 17). Which Words to Teach: Word Selection in the Age of Large Language Models. In Encyclopedia. https://encyclopedia.pub/entry/60091
Hiebert, Elfrieda H.. "Which Words to Teach: Word Selection in the Age of Large Language Models." Encyclopedia. Web. 17 September, 2026.
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English has a large and historically layered lexicon. The usual approach to vocabulary instruction, finding the unfamiliar words in an assigned passage and teaching them, improves comprehension of that passage but transfers poorly to broader measures. The problem has not been a lack of theory about which words merit instruction; frameworks for principled word selection have been well developed for more than a decade. What was lacking was a way for educators to apply those criteria, with the necessary corpus, morphological, and psycholinguistic analyses largely confined to research databases and the expertise of specialists. This review argues that large language models do not change what makes a word worth teaching, but rather they change what is now available to apply those criteria. Educators can now apply the criteria to a particular text and grade level using ordinary language. Drawing on four texts about hurricanes that hold the subject constant while varying genre and grade, the review develops three applications: domain-structured selection for informational text, semantic-cluster mapping for narrative, and the identification of a text’s latent lexicon that is particularly germane to narrative texts. It closes with the limits of these tools and identifies the points where teacher judgment is still required and the validation studies they still need.
vocabulary instruction
word selection
large language models
reading comprehension
informational text
narrative
semantic clusters
disciplinary literacy
word frequency
English has one of the largest lexicons of any language, with some 600,000 word forms, more once scientific and technical terms are counted, recorded in the Oxford English Dictionary. Counting forms understates the task because one form often carries many senses, and to know a word in one sense is not to know it in another.
No reader meets more than a fraction of that range, but the fraction that matters is enormous. For printed English in Grades 3–9, Nagy and Anderson [1] estimated roughly 88,500 distinct word families—much of which reflect morphology’s generativity because a single base word such as govern yields governs, governor, government, governance, and more [2]. The families are unevenly weighted; a core of about 2500 carries most of the words in any running text [3]. The remaining tail is immense, and it is the tail, not the core, that distinguishes a strong reader from a struggling one. Most rarer words are met in print rather than speech, as shown by research [4] indicating that a children’s book offers rare words at a higher rate than the conversation of college graduates.
Against a lexicon of that size, the conventional approach to teaching vocabulary has produced strikingly little. For decades, publishers and teachers have proceeded identically: examine an assigned passage, mark the words readers are unlikely to know, and teach those. The evidence is sobering. In a meta-analysis of vocabulary interventions, Elleman et al. [5] found a moderate effect on comprehension measures built around the taught words but almost none on standardized ones. Wright and Cervetti [6] reported the same divide, and Cervetti et al. [7] found no reliable effect on distal vocabulary measures. Teaching the words in a passage helps a student read that passage, but it does little to build the broader lexicon the next text requires.
The deficiency does not stem from uncertainty about which words to teach. Nagy and Hiebert [8] identified the features that make a word worth teaching, and a subsequent encyclopedia entry [9] extended the framework as large school-text corpora and word-feature databases became more readily available. Together, these two reviews supplied a comprehensive basis for selection of vocabulary from texts. What limited the application of these criteria was not the theory but rather access, because the analytic means of implementing the criteria lay in research databases and the judgment of specialists. Educators simply did not have the time or resources to consult a frequency guide, cross a domain analysis against a morphological database, and arrive at a grade-calibrated word set for a specific text.
But now that condition has changed. Large language models (LLMs) place an analytic capacity once confined to research databases and specialists within reach of practitioners and curriculum developers through an ordinary-language interface. Educators can pose questions about the lexicon and in seconds receive an answer scoped to a grade, domain, or text.
The present review summarizes the two previous reviews on word selection and identifies the affordance unavailable when they were written and what it changes for the selection of vocabulary. A closing section considers the discipline the capability demands if it is to serve readers rather than merely accelerate the production of plausible word lists.
The status of what follows should be stated plainly. This is a conceptual review with illustrative demonstrations that claims that the constraint on principled word selection has been accessed rather than theory and that the constraint has now been removed. The four hurricane texts and the three tables are demonstrations of what established selection principles look like when applied through a language model. They are not empirical findings because they do not establish the reliability, stability, or reproducibility of LLM-based word selection, and no claim is made here about whether another model, prompt, or run would return the same sets. Section 2.3 reports how the illustrations were produced and checked, and Section 5.4 sets out the validation that would be required before such outputs could be treated as evidence.
The review proceeds as follows. Section 2 sets out what word-selection theory requires, describes how language models represent the lexicon, and reports the procedure behind the illustrations. Section 3 and Section 4 develop three applications: domain-structured selection for informational text, semantic-cluster mapping for narrative, and identification of a text’s latent lexicon. Section 5 appraises the capability, specifies what educators should verify before using its output, and identifies the research still needed.
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