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Review

Which Words to Teach: Word Selection in the Age of Large Language Models

by
Elfrieda H. Hiebert
TextProject, Santa Cruz, CA 95060, USA
Encyclopedia 2026, 6(9), 201; https://doi.org/10.3390/encyclopedia6090201
Submission received: 4 June 2026 / Revised: 24 August 2026 / Accepted: 28 August 2026 / Published: 14 September 2026
(This article belongs to the Collection Encyclopedia of Social Sciences)
Versions Notes

Abstract

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.

1. Introduction

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.

2. What Word-Selection Theory Requires and What Limits Its Use

The theory of word selection is well developed, yet until recently, teachers had no way to apply it. What language models supply is not a better theory but rather a means of querying its principles.

2.1. A Theory of Word Selection Without a Means to Apply It

The lexicon of English was assembled in three historical layers: a Germanic Anglo-Saxon core, a Norman French layer later deepened by Renaissance Latin, and a Greek layer introduced through scientific and academic discourse [10,11]. The result is that many concepts are named more than once in the English lexicon, as in earth, terrestrial, and geologic. Anglo-Saxon words tend to be short, frequent, concrete, and learned early; their Latinate and Greek counterparts run longer, rarer, and more abstract and are concentrated in academic writing [12,13]. The lexicon is stratified as well as large, and the stratification is what makes selection difficult: the words that matter most for academic reading are those that everyday speech is least likely to supply.
Two earlier reviews frame the present argument. Nagy and Hiebert [8] addressed the features that make a word worth teaching and how those features apply across text types. At its core was a taxonomy of eight features grouped under the four roles a word plays: a role in the language, indexed by frequency and dispersion; a role in the lexicon, set by morphological family and relatedness to known words; a role in students’ knowledge, captured by familiarity and conceptual difficulty; and a role in the lesson, defined by importance to a text and recurrence across the curriculum.
Two claims follow. First, no single variable yields a defensible set of words. Second, the weighting is genre-specific: the rare words of a story embellish and texture it, whereas the rare words of a science text often carry the concepts a reader must acquire.
Three of the four roles identified by [8]—frequency and dispersion, family membership and relatedness, and importance and recurrence—can be derived from a corpus. The fourth cannot. A word’s place in students’ knowledge is a property of the reader, not the word; selection theory has met this asymmetry by letting grade level stand in for what students know, a serviceable proxy, though only a proxy. The affordance described here reaches the first three roles and leaves the fourth untouched.
A decade later, an encyclopedia entry [9] extended the framework, drawing on resources that had, in the previous decade, become available. Large corpora of school texts and databases tagging words for age of acquisition [14] and concreteness [15] enabled analysis at a scale the earlier chapter could only anticipate. The SCOPE metabase (South Carolina Psycholinguistic Metabase; [16]) typifies these resources, consolidating major databases into one queryable collection of 245 lexical variables for over 100,000 words. Values once assembled by hand from scattered sources could now be drawn from one standardized account.
The 2023 entry also advanced the possibility that anchors the present argument: words might be selected for the constructs that organize a genre, rather than for a single passage, so learning transfers from text to text. A pair of literary words such as slender and gallant might then teach the function of character traits in a plot rather than two isolated meanings.
Both accounts described the analysis that sound selection requires, but neither could place it within practitioners’ reach. The tools existed, but only specialists could use them.

2.2. How Language Models Represent the Lexicon

What distinguishes LLMs from earlier resources is access to the distributional structure of the lexicon—the patterns by which words co-occur, the registers in which they cluster, the domains they span, and the frequencies at which they appear. The premise is old: in Firth’s (1957) formulation [17], a word is known by the company it keeps. Corpus linguists made that premise their working material [18,19], and computational work rendered it a method, representing each word as a point in a space whose dimensions are its co-occurrences [20,21]. There, words used in similar contexts sit near one another, and relations among them—near-synonymy, domain membership, and register—become measurable rather than intuited. An LLM is built on such representations; what is newly available is not knowledge of words but rather the capacity to interrogate that structure in ordinary language.
What earlier accounts told publishers and researchers how to do, the capability now lets educators do directly in ordinary language and within seconds. Three uses follow: the first maps onto informational text; the second, onto narrative; and the third, onto narrative as well as the journalistic articles that convey topical knowledge.
To make the affordance concrete, the entry draws throughout on four texts about hurricanes chosen so that genre and grade vary while the subject holds constant. Two are informational, Hurricanes [22] and Hurricanes: Earth’s Mightiest Storms [23], and two are narrative, Nora’s Ark [24] and Ninth Ward [25]. Within each genre, two are written for late-primary [22,24] and two for middle-school readers [23,25]. Because the topic stays fixed, differences in vocabulary can be traced to genre and grade rather than to a change in topic.

2.3. Producing and Checking the Illustrations

The illustrations in Section 3 and Section 4 were generated with GPT-5.5 on 3 August 2026, through the standard conversational interface with default settings; no sampling parameters were adjusted, and each prompt was issued once.
Three prompts produced the three tables. For Table 1, the model was asked to extract weather and storm vocabulary from [22,23] and to align the result with the Next Generation Science Standards (NGSS) [26] for Grades 3–5 and middle school. For Table 2, it was asked to organize the semantic cluster for fear in Nora’s Ark and Ninth Ward by centrality, register, and typical grade of appearance. For Table 3, it was asked to identify phrases in the two narratives that enact a construct without naming it. Full prompts are provided in Appendix A.
For every item the model returned, the author then checked against the source. Terms attributed to a trade book were verified against the book, and those attributed to a standard were verified against the published NGSS performance expectations, clarification statements, and disciplinary core ideas. Where a book expresses a concept without using the technical term, the entry is marked parenthetically rather than counted as an occurrence; those judgments are the author’s, not the model’s.
This procedure establishes that the entries in the tables are present in the sources named but does not establish that the same model would return the same set on another run, under a different prompt, or in a later version, and no comparison was made against vetted corpora such as [27,28]. Therefore, the tables are provisional demonstrations rather than validated outputs and should be read as such throughout.

3. Domain-Structured Selection for Informational Text

Informational text is the form in which disciplines, such as science, history, economics, and geography, transmit what they know to readers learning to think within them. Each discipline organizes its knowledge into domains—coherent bodies of concepts, relationships, and ways of reasoning that distinguish one field of inquiry from another [29]—such as hurricanes forming a domain within meteorology; ecosystems, within biology; and World War II, within history. A domain is the organized field of inquiry from which any single text is a slice and is larger than a single textbook topic.
Each domain comes with its own vocabulary. Words such as photosynthesis and isotherm name the concepts of a field. Learning to read and write in a field is, in substantial part, learning its specialized lexicon and the discourse patterns through which it organizes claims and evidence [29]. Such vocabulary cannot be acquired through general reading: a reader who never reads science will not meet catalyst or electron, nor will one who never reads history meet abolition or armistice.
A second band of vocabulary, described as general academic vocabulary [12,13], cuts across these disciplinary lexicons. Words such as analyze, factor, distinguish, and sequence mark formal academic discourse without belonging to any one field. They form the connective tissue of disciplinary writing—the reasoning verbs and abstract nouns by which a field holds its claims together. A reader who can name the parts of a hurricane but cannot follow what it means for one factor to contribute to another will not understand the passages in which those parts appear.
Rather than asking which words in a passage might prove unfamiliar, educators can ask which words organize a domain. The warrant is well established. Content-anchored instruction—words taught as members of a coherent knowledge domain rather than as items on a list—produces deeper word knowledge on distal comprehension measures [30], and domain knowledge supports comprehension even of texts whose specific words are new to the reader [31].
To illustrate, an LLM was prompted to extract the topics and vocabulary for weather and storms from two trade books on hurricanes—[22] for the primary grades and [23] for the middle grades—and to match them to the NGSS for Grades 3–5 and middle school. Table 1 summarizes those results. In the NGSS, domain terms appear only within the performance expectations, clarification statements, and disciplinary core ideas; there are no vocabulary lists. In Grades 3–5, the word hurricanes does not appear, subsumed under general headings of weather and hazards (e.g., weather conditions, seasons). Only at the middle-school level is the term hurricane named directly.
Two patterns emerge from the comparison. First, both trade books exceed the Grade 3–5 standards in specificity. Where the standards keep the vocabulary general, such as weather, hazard, and flooding, the books name hurricane, eye, and storm surge, introducing domain language at a level the standards do not. The second observation is the more consequential. The trade books’ most distinctive domain terms—eye, eyewall, storm surge, surge, typhoon, cyclone, evaporation, cumulonimbus, updraft, Bermuda High, landfall, and Hurricane Hunters—do not appear in any of the Grade 3–5 standards. Students meet this domain vocabulary in the trade book rather than in the standard.
In terms of the [8] taxonomy, this application operates on two of the four roles. It addresses the word’s role in the language, because domain terms are by definition low in general frequency and narrow in dispersion, and its role in the lesson, because a term’s importance to a text and its recurrence across a unit are what make it worth teaching here. It does not address the word’s role in students’ knowledge, which no corpus analysis can supply.
Both books belong to informational nonfiction, but the genre is not uniform across the developmental range. Books for yowunger readers, such as Gibbons’s, favor direct, non-narrative exposition and classification, whereas those for older readers more often adopt a quasi-informational, narrative-bearing treatment—pairing expository explanation of how a phenomenon works with a sustained narrative of the real events that exemplify it [32,33,34]. Lauber’s volume belongs to the latter category. This developmental difference in form, rather than any difference between trade books and other kinds of text, shapes the two vocabulary profiles reported here. A standards-aligned textbook chapter in which a phenomenon such as hurricanes figures as one bounded unit of coverage among other weather topics would presumably yield a profile different from either—though that comparison lies outside the present analysis.

4. The Unique Vocabulary of Narrative Texts

Where an informational text’s vocabulary is the domain’s terms, a narrative renders an experience: what a character does, feels, and undergoes. Its vocabulary supplies the words that make movement, feeling, and circumstance vivid. A story has no standard set of domain words; its words attach to the elements of story structure through which readers organize a narrative: characters and the roles they fill, the traits that mark them, the settings they move through, the emotions they feel, the problem that sets the story in motion, and the actions that resolve it [35,36]. A narrative’s vocabulary gathers around these elements [37] and proceeds by enactment rather than statement—showing how a character acts and feels.
Two features of narrative vocabulary follow. First, writers rarely settle for a single word where a story calls for a precise meaning; they draw a member from a family of related words—a semantic cluster—that fits the register and intensity of the context. Second, the words a story builds most fully are often those it never names: the writer dramatizes a concept and leaves the reader to supply its name. The two are connected. To enact a concept it does not name, a narrative assembles it from particular events and descriptions.

4.1. Semantic Clusters in Narrative Texts

Clusters of words around a construct such as fear are internally organized. A small number of neutral members—such as afraid and worried—occur frequently, distribute broadly, and are acquired early [38,39]. The remaining members are marked terms—more specific in meaning and therefore less frequent, more register-bound, and narrower in collocation [40,41]. Thus, vocabulary growth entails not only acquiring words but also elaborating the relations among them [42,43].
Treating the semantic cluster as the unit of instruction is not new. Ref. [44] held that words are learned most effectively in relation to their semantic neighbors, and [45] organized more than 7000 words into superclusters, clusters, and miniclusters. Semantic gradients operationalize this principle, arranging members along a continuum of degree to make near-synonym distinctions explicit [46,47].
LLMs can now generate such organizations on demand. Given words from a narrative text and a target grade level, a model can identify the broader semantic cluster; distinguish neutral from marked members; and arrange words by degree, register, and typical age of appearance. Table 2 illustrates this process for the cluster of fear in Nora’s Ark and Ninth Ward. The resulting organization reveals a developmental pattern: Grade 3 narratives rely on neutral, high-frequency members, whereas Grades 5–6 narratives increasingly add marked terms that convey finer distinctions. Based on this view, vocabulary grows by differentiation within a semantic field rather than by accumulation.
This approach has empirical support. In [6], a review of 36 vocabulary interventions, the only studies with significant effects on distal comprehension measures taught words in semantic clusters rather than in isolation [48,49]. Therefore, transfer depends on the semantic systems in which words participate. For comprehension, to know a word is to know its relations to its neighbors—what it shares, how it differs, and where it sits in a broader lexical network [50]. This application operates on the role the taxonomy names, but that frequency counts cannot reach: the word’s role in the lexicon—its relatedness to words the reader already holds. Grade band is again standing in for the reader, and the same caution applies.

4.2. A Text’s Latent Lexicon

The two prior applications locate words that appear in a text or organize its surface; a third looks for what the text leaves unsaid. Because narrative enacts meaning rather than asserting it, the words a story most depends on are frequently the ones it never states—words the reader must supply—together forming the text’s latent lexicon [41,51].
The term “latent lexicon” requires definition because it is used here in a narrower sense than its ordinary reading would suggest. By a text’s latent lexicon I mean the set of concepts a text constructs through particulars while withholding the label such that a reader must supply the word to name what the writing has shown. It does not mean words a given reader happens not to know or the words a topic might be expected to carry. It is a property of the text’s method rather than of the reader’s vocabulary: a narrative that shows a character improvising a rescue and never calls her resourceful has placed resourceful in its latent lexicon whether or not any particular reader knows the word.
The latent lexicon extends even to the vocabulary of a knowledge domain. The story in Ninth Ward [25] is set inside a hurricane’s flood, yet names almost none of the terms a science text would use, such as storm surge and evacuate. Instead, the story supplies the experience the terms denote. The surge is the water that rises until it fills the house, and the failure to evacuate appears in the neighborhood’s barbecue.
The unnamed words that matter most are those bound to story structure. A character is not called resourceful; instead, she is shown improvising a rescue from the materials at hand. Supplying the word resourceful is to perform an inference, which is the basis of skilled comprehension. Students who read accurately yet comprehend poorly typically fail to draw the inferences a text invites [52]; instruction in inference strengthens comprehension itself [53]. To register that a character trudges is to apprehend effort and fatigue without the word; dread rather than worry signals harm both certain and near. Such features accrue into the situation model the reader builds, and supplying the unnamed word is itself an elaborative inference—the unstated content narrative comprehension demands [54,55,56]. An LLM can identify the phrases that mark this latent vocabulary, as Table 3 illustrates. Such material supports an inferential stance: a word is a claim a text makes, not an entry to be looked up. Comprehension depends on depth of word knowledge rather than acquaintance. A word does its work when its form, meaning, and use cohere, and that coherence grows as the reader meets the word in varied contexts [57,58].
In terms of the [8] taxonomy, this third application is the least conventional of the three, because the words it returns are not in the text at all; it nonetheless engages the same roles. It bears most directly on the word’s role in the lesson: a word that names what a narrative has spent pages enacting is, by that fact, important to the text, and the story-structure constructs that carry such words—trait, motive, circumstance, and resolution. It also engages the word’s role in the lexicon, because a latent word is reached through its relations to the words the text does use. The word’s role in the language is engaged only obliquely: latent words are typically the marked rather than the neutral members of their clusters and are therefore low in frequency and narrow in dispersion. The fourth role—the word’s place in what a particular student already knows—is again untouched, and it weighs more heavily in this application than in the other two, because whether supplying an unnamed word is a productive inference or an impossible one depends on what the reader already holds. Grade band, as in Section 3 and Section 4.1, is standing in for the reader.

5. The Affordance, Its Limits, and Directions for Research

This section takes the measure of the capability—the distributional evidence that grounds it, the three points at which educator judgment remains indispensable, and the validation still owed against vetted corpora.

5.1. The Affordance

Across all three—mapping a domain, ordering a gradient, and identifying a text’s latent lexicon—the LLM performs one operation: in seconds, it returns a neighborhood of related words scoped to a grade level and a target text. This does more than save time for educators and curriculum developers; it externalizes a structure that vocabulary research has long treated as central but that educators could rarely reach directly.
The grounds lie in how the model represents language. As established earlier, a word’s meaning is constituted by the contexts in which it occurs [17,59]. LLMs operationalize that claim: their embeddings encode each word’s distribution as a position in a high-dimensional space where proximity approximates semantic relatedness [20,21,60]. In this representation, a semantic cluster is a bounded region of that space. Here, two literatures converge: reading acquisition builds the paradigmatic organization of the lexicon [61,62], and the richness of that organization predicts comprehension [6,57]—the same organization the model recovers from text. In returning a cluster, the model surfaces structure that vocabulary scholarship has identified as consequential, structure that once demanded specialist expertise and more time than an instructional planning cycle allowed.

5.2. The Limits

Three constraints qualify the affordance, each marking a point where human judgment remains indispensable. The first is intrinsic to distributional representation. Co-occurrence statistics register that two words share contexts but do not specify the relation binding them. Synonyms, antonyms, and loose associates occupy adjacent regions of the vector space [63]. The model proposes a semantic field, to which educators are responsible for distinguishing its neutral core from its marked members and judging which contrasts a reader at a given grade is prepared to hold.
The second concerns the gap between a candidate set and a curriculum. The model returns words; it does not decide which distinctions to draw or which members to teach. That determination is pedagogical, and the ease of generating output makes an ungrounded selection as available as a principled one. An educator who requests “important words about hurricanes,” ungrounded in a corpus or domain analysis, may receive a list that reads plausibly yet misrepresents the conceptual field.
The third, and most consequential, follows from the model itself. An LLM learns from the form of language—the patterns in how words are used—not from the world those words describe [64]; its fluency with distributional structure can be mistaken for a grasp of meaning. The implication is clear: a model is a strong guide to how words pattern but a weak authority on whether a word is faithful to a discipline or central to a concept. The model can propose words, but it cannot verify them [65]. Therefore, the quality of any selection is bounded not by model capacity but rather by the theory educators bring to the prompt.

5.3. What Educators Should Verify

These limits imply a specific set of checks. Therefore, before a model’s output becomes an instructional word list, it is worth asking seven questions—a rigorous but manageable check on what the model produces.
  • Domain accuracy. Is the word used this way in the field? A model can return a term that patterns like disciplinary vocabulary without being one the discipline recognizes, or it can attach a term to the wrong level of a hierarchy. This check requires either subject knowledge or a standards document, and it is the check a general-purpose model is least able to perform on its own behalf.
  • Grade appropriateness. Is this word plausible for these readers at this point? Grade is the model’s only proxy for the reader, and it is a coarse one. Databases of age of acquisition [14] offer a firmer anchor than a model’s estimate.
  • Conceptual centrality. Does the word carry a concept the text depends on, or does it merely appear in the text? Distributional proximity does not distinguish the two, and the distinction is the whole of what makes a word worth instructional time.
  • Frequency and dispersion. Is the word frequent enough and spread widely enough across texts to repay teaching? A model’s sense of frequency is impressionistic; a frequency guide [27,28] is not.
  • Morphological relevance. Does the word belong to a family whose other members the reader will meet? A word that opens a productive family returns more than an isolated one, and the model will not weight this unless asked.
  • Students’ prior knowledge. Do these particular students already hold this word? This is the taxonomy’s fourth role, and neither the model nor any corpus can answer it. Much of what the vocabulary programs teach is already known to the students receiving it [66,67], which makes this the check with the largest consequence for instructional time.
  • Instructional purpose. What is this word being taught for? A word selected to unlock one passage, a word selected to build a domain, and a word selected to sharpen a semantic distinction are three different decisions, and the model cannot make any of them.
The first five checks can be run against an external source. The sixth requires assessment of the students themselves, and the seventh is a judgment about the curriculum. This is the sense in which educator judgment remains necessary, not as a general caution about machine output, but as seven specific determinations the model is not positioned to make.

5.4. Directions for Research

Realizing the potential of these tools will require evidence not yet available. The first priority is empirical validation: establishing whether the word sets, recurrence profiles, and cluster maps these models generate are calibrated to grade and domain and how they compare with selections from vetted corpora (e.g., [27,28]).
A second priority is the design of the prompts themselves. A model’s output is only as sound as the request that produces it, and a sound request must specify grade, domain, and corpus grounding—knowledge that cannot be assumed of every teacher. Building that expertise into the prompt so that a principled selection does not require the user to already know how to ask for one is a problem of instructional design as much as of language technology.
A third priority addresses a long-standing gap in the [8] framework, which is the taxonomy’s fourth role—the word’s place in what a particular student already knows. The omission is not trivial. Much of the vocabulary core programs teach is already known to the students receiving it [66,67], and instruction aimed at words a class already commands is withheld from those it lacks. Digital assessment suggests how the reader’s side might become legible at comparable scale. The Core Vocabulary Assessment within the Rapid Online Assessment of Reading [68] suite is automated, browser-based, and not proctored, returning for a whole class an estimate of which words students know and which they do not. Small scale rather than comprehensive, nonetheless, it shows what becomes possible once a reader’s word knowledge can be sampled as the vocabulary of texts can be.

6. Conclusions

A theory of word selection has been well established. Its bases—domain structure, narrative structure, frequency and dispersion, morphological family, and conceptual centrality—were sound when [8] set them out and [9] extended them. What was missing was a means of applying them within the time a lesson allows. Neither the lexicon nor the theory has changed; what has changed is the locus of access. An analytic capacity once confined to databases and specialists can now be queried in natural language at the scale of ordinary practice, but whether it improves the words students learn depends on the judgment brought to it, thus making selection, which establishes what words a domain and genre warrant, and assessment, which a given student still requires, both necessary.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

During the preparation of this manuscript, the author used Claude Sonnet 4.5 (Anthropic) for the purposes of revising the text in response to reviewers’ queries, including verifying citations and documenting the changes made. The author has reviewed and edited the output and takes full responsibility for the content of this publication. All model-generated material reported here is contained within the article: the prompts appear in Appendix A and the verified output appears in Table 1, Table 2 and Table 3.

Conflicts of Interest

Elfrieda H. Hiebert serves as president and CEO, on a non-compensated basis, of TextProject, a not-for-profit company. The author declares no conflicts of interest.

Appendix A. Prompts for the Three Thematic Analyses

The three prompts are reproduced here verbatim, as they were entered; book titles are set in italics for readability. The descriptions of the prompts in Section 2.3 are paraphrases of the wording that follows.
Prompt for Table 1: Extract weather and storm vocabulary from Gibbons (2003) [22] and Lauber (2000) [23] and align the results with the Next Generation Science Standards (NGSS) for Grades 3–5 and Middle School.
Prompt for Table 2: Organize the semantic cluster for words meaning fear in Nora’s Ark and Ninth Ward by centrality, register, and typical grade of appearance.
Three-part prompt for Table 3:
1st Prompt: Please identify the 7 to 10 central themes of each book: Nora’s Ark and The Ninth Ward.
2nd prompt: From these central themes for each of these two books, can you identify 7 themes that are shared in the two books? Please use a single word to identify each of the themes.
3rd prompt: For the 7 themes of resourcefulness, perseverance, composure, grief, adaptation, foreboding, solidarity: Pull textual evidence for each theme from each book.

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Table 1. NGSS domain vocabulary mapped to two trade books 1.
Table 1. NGSS domain vocabulary mapped to two trade books 1.
Grades 3–5 (Standards 3-ESS2-1, 3-ESS2-2, 3-ESS3-1, 4-ESS3-2)
NGSS Domain TermGibbons (2003) [22]: Grade 3 Lauber (2000) [23]: Middle School
weather“weather officials”“weather scientists”
season“season” (first storm of season)—
temperature“temperature”; “water temperature”(proxy: “80 degrees F or warmer”)
precipitation(everyday term: “heavy rains”)(everyday term: “rain”, “torrents”)
wind/wind direction“winds”, “wind speed”“winds”, “wind direction”
climate/region— (named oceans only)“region”
weather-related/natural hazard(proxy)(proxy: damage, destruction)
flooding“flooding”, “massive flooding” “flooding”
Middle School (Standards MS-ESS2-5, MS-ESS2-6, MS-ESS3-2)
NGSS Domain TermGibbons (2003) [22]: Grade 3Lauber (2000) [23]: Middle School
air mass(proxy: rising air)(proxy: air flow)
high/low pressure— (“air pressure” only)“high pressure”, “low pressure”
humidity(proxy: “moisture”, “moist air”)(proxy: “moisture”, “moist air”)
condensation“condensation”“condense”, “condenses”
atmospheric/oceanic circulation(proxy: “updraft”, “rotate”, “spinning”)(proxy: “spiraling winds”, steering winds)
severe weather/natural hazard(concept only)(concept only)
hurricane (named)“hurricane”“hurricane”
tornado— (typhoon, cyclone instead)—
flood“flooding”“flooded”
forecast“forecasting”, “predict”, “predictions”“forecast”, “forecasters”
catastrophic“catastrophic”(“proxy: crushed”, “swept clean”)
mitigate/reduce impacts(proxy: “evacuate”, cover windows)(proxy: goal to end “terrifying surprise”)
magnitude(proxy: “category”, Saffir-Simpson scale)—
frequency(proxy: “once every five years”)—
satellite“satellites”, “computer model”— (older tools only)
1 A dash (—) marks a term absent from the book; a parenthetical marks a concept present without the technical word. All entries were generated by a language model and then checked by the author against the trade books and the published NGSS documents; see Section 2.3. No validation against external corpora or independent expert judgment was conducted, and the table should be read as a provisional demonstration.
Table 2. Semantic cluster for fear in narrative, by centrality and grade band 2.
Table 2. Semantic cluster for fear in narrative, by centrality and grade band 2.
Cluster MemberStatusRegister/NuanceGrade Band
worriedneutralgeneral unease3
scaredneutralgeneral fear3
afraidneutralgeneral fear3
shiverneutralthe body’s sign of cold or fear3
tremblingmarkedthe body’s response to fear5–6
dreadmarkedthe slow approach of something feared5–6
terrormarkedthe extremity of the feeling5–6
2 All entries were generated by a language model and then checked by the author against the two narratives; see Section 2.3. Verification established that each word listed occurs in the books named, but the assignments of neutral or marked status, of register and nuance, and of grade band are the model’s classifications, reviewed by the author rather than independently validated. No comparison was made against age-of-acquisition norms (e.g., [14]), frequency guides (e.g., [27,28]), or independent expert judgment, and the table should be read as a provisional demonstration.
Table 3. Cues for the latent lexicon 3.
Table 3. Cues for the latent lexicon 3.
Unnamed ConstructNora’s Ark: Grade 3Ninth Ward: Middle School
ResourcefulnessBread loaded into “my old baby carriage”; horse Major led indoors because “He’s big. He’ll add heat to the place” when there is no stove; drinking water scooped from Lafleur’s rowboat.Candles, matches, flashlights, and blankets gathered into one room; water heated into hot-water bottles; the floating tree trunk wielded “like a lance” and worked “like playing pool” to knock the boat free; teaching TaShon to “doggy paddle”
PerseveranceRefusing to quit: Grandma and Wren row out into water “full of furniture and trees and dead animals” to find Grandpa; “We’ll have to start over,” answered with “We can do that.”“Mama Ya Ya wouldn’t want us to give up. Pick up the trunk”; “what else we going to do? Try or not try?”; “One. Two. Three. Punch”; “Fight, Lanesha”; “I am strong. Not scared.”
ComposureGrandma “made of granite”; “For once, Grandma didn’t argue”; calmly baking twenty-seven loaves before the water rises“Neither can I. You don’t see me crying”; “Twenty minutes for each whole step … two hours left”; “Tell myself not to be afraid.”
GriefMourning a loss: Grandpa “was crying as we rowed away” from the trapped cow; “All our cows drowned … The house, the barn, the horses, they’re all gone.”Holding Mama Ya Ya’s hand “until her hand slips out of mine,” then her chest for breath; “It’s only then I cry. My hand covering my mouth, though no sound is coming out.”
AdaptationThe new house revealed as “really an ark”; the hoofprints kept in the floor as a chosen reminder; “start over” recast as something they “can do.”The butterfly that grows “from a gray white cocoon into something colorful”; “I’ve been born to a new life”; “Becoming grown in a new way”; TaShon seen anew—“He’s a butterfly, too.”
ForebodingGathering threat: “Life in Vermont was about to change forever”; no one imagining “nine inches of rain”Dramatized through unnatural silence (“A VACUUM”) and ghosts “LOITERING”—though here the book names the construct: “an OMEN: OF BAD THINGS TO COME.”
SolidarityTwenty-three people and a kitchen of animals sheltering together; shared salt pork, dried apples, and songs—though closing line names it: “neighbors helping neighbors.”The pre-storm cookout (“No sense letting meat waste”); covered plates left at the door—“Neighbors have shared extra food. I guess we are surviving this storm together.”
3 All entries were generated by a language model and then checked by the author against the two narratives; see Section 2.3. Verification established that each quoted phrase appears in the book named, but the naming of the unnamed construct and the assignment of particular phrases to it are interpretive judgments that go beyond presence in the source text. No independent expert coding or inter-rater agreement was obtained, and the table should be read as a provisional demonstration.
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