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\title{An Interactive System for Generating Revisable Grammar Lessons for Extremely Low-Resource Languages Without Expert Annotation}

\author{Sebastien Christian \\
  Research Center for Pacific Societies and Humanities\\
  University of French Polynesia, CNRS\\
  \texttt{sebastien.christian@upf.pf} \\
}

\begin{document}
\maketitle

\begin{abstract}
Endangered-language teaching often faces two practical bottlenecks: the scarcity of experts able to produce pedagogical grammars, and the dependence of most approaches on expert linguistic annotation. We present DIG4EL, a human-in-the-loop system for extremely low-resource languages that addresses both constraints by combining lightweight concept-based annotation, typological inference, structured sentence-pair augmentation, document retrieval, and constrained language model generation. Rather than aiming to produce definitive grammatical descriptions, the system generates revisable grammar lesson drafts grounded in heterogeneous evidence, including elicited sentence pairs, free translation pairs, and descriptive documents. The interface is designed so that speakers, teachers, and other language practitioners without formal linguistic training can contribute usable data, inspect intermediate inferences, and control source selection during generation. We describe the architecture, user workflows, and initial deployment experience in real-world revitalization settings. The contribution of the paper is an implemented workflow for early pedagogical draft generation under extreme data scarcity, not a controlled evaluation of pedagogical effectiveness.
\end{abstract}

\section{Introduction}

Two long-standing bottlenecks make teaching the grammar of endangered languages difficult. First, most formal documentation workflows that support grammatical description rely on expert linguistic annotation. Second, the number of linguists available to produce pedagogical grammars is far below the scale of the need. For many communities, waiting for expert annotation or expert-authored teaching materials means waiting indefinitely.

This paper starts from a different operational assumption. When the goal is not a definitive reference grammar but a revisable pedagogical draft, extremely small amounts of data can already be useful. A few hundred sentence pairs, lightly enriched through annotations that language speakers can produce themselves, may be sufficient to generate lesson drafts for foundational grammatical topics. In this setting, expert linguistic annotation remains highly valuable when available, but it is not treated as a hard prerequisite for producing useful teaching materials.

We focus on endangered languages with extremely limited data, typically ranging
from a few hundred to a few thousand transcribed sentence pairs, and lacking
standardized pedagogical grammars. We refer to this setting as
\textit{extremely low-resource languages} (ELRLs)\footnote{We use the term
\textit{extremely} because, for instance, \citet{nllb_team_scaling_2024}
consider languages with fewer than one million sentence pairs
\textit{low-resource}, and languages with fewer than 100,000 sentence pairs
\textit{very low-resource}. These thresholds remain orders of magnitude above
the data conditions encountered in most community-led documentation and
revitalization efforts.}.

Teaching grammar is an important component of language revitalization, as many
community members learn their ancestral language as an additional language
\citep{kachinske_interaction_2019,nabizadeh_effect_2016}. Yet the production of
pedagogical materials remains a major bottleneck. Teachers are often expected to
create structured lessons despite limited time, limited formal preparation, and
the absence of classroom-ready resources. Comprehensive grammars typically take
years to produce \citep{woodbury_2011}, and even when they exist, they usually
require substantial transformation before they can be used in teaching
\citep{sapien_using_2019}. At the same time, many teachers report feeling
unprepared to create grammar-focused teaching materials
\citep{chaudhary_teacher_2022}.

Existing tools and methods address parts of this problem but do not remove these
bottlenecks. Linguistic software such as FieldWorks Language Explorer
\citep{FLEx2025} supports corpus management and lexicon building, but does not
generate pedagogical lessons and still assumes substantial expertise. Earlier
systems such as PAWS \citep{black_paws_2009} assist in drafting grammatical
descriptions, but remain oriented toward linguistic analysis and require expert
intervention. More recent approaches such as BASIL
\citep{howell_building_2022} and Autogramm \citep{corro_sparse_2024} focus on
extracting formal grammatical structure from annotated data, but are not
designed for pedagogical output or for operation under extreme data scarcity.
Retrieval-augmented natural language processing (NLP) methods help retrieve relevant  sources from a corpus, but they do not, by themselves, solve the
problem of turning sparse, heterogeneous evidence into usable pedagogical drafts
for non-specialist users.

We present an interactive system designed to reduce both bottlenecks. It enables
speakers, teachers, and revitalization supporters without formal linguistic
training to contribute usable data and generate editable grammar lesson drafts.

The system combines existing typological information, probabilistic inference,
structured sentence augmentation, retrieval, and constrained generation by
language models, used here for their ability to reason over linguistic data
\citep{Zheng2025LogiDynamics} and format outputs. It is designed around
usability, inspectability, and the progressive enrichment of available data. Its
outputs are explicitly provisional: users are expected to inspect, revise, and
adapt them before use. The system trades quality for existence. 

The broader governance approach of the system is intended to align with CARE \citep{carroll_care_2020} principles and, where appropriate, with FAIR \citep{wilkinson_fair_2016} principles in ways compatible with community governance requirements.

\paragraph{Contributions}
\begin{itemize}
\item We formulate grammar-lesson generation for ELRLs as a distinct problem
characterized by two practical constraints: scarce expert annotation and scarce
linguist-authored pedagogical grammars.
\item We present a human-in-the-loop architecture that combines lightweight
concept-based annotation, typological inference, structured sentence-pair
augmentation, retrieval, and constrained generation to produce revisable lesson
drafts.
\item We describe interface and workflow choices that enable non-specialists to
contribute structured evidence, inspect intermediate inferences, and control
source use during generation.
\item We report initial deployment experience in real-world revitalization
settings, suggesting that the workflow can be operated by non-specialist users
under deployment conditions.
\item We release the source code as open-source software and provide access to a
live interface.
\end{itemize}

\paragraph{Scope: focused lessons rather than full grammars.}
We deliberately target focused grammar lessons rather than full descriptive
grammars. Under extreme data scarcity, bounded outputs on a user-selected topic
are faster to generate and easier to revise.

\paragraph{Software availability.}
The version of the system described in this paper is archived on Zenodo
\citep{christian_dig4el_2025}. Ongoing development takes place in the public
GitHub repository at \url{https://github.com/alterfero/dig4el}. The public
interface is available at \url{https://dig4el.org}.

\section{System Overview}
\label{overview}

The system can operate with any one of three input sources, although it benefits
from combining them:

\begin{itemize}
\item \textbf{Annotated Elicited Pairs (AEPs):} a predefined set of English
sentences and sentence segments to be paired with their translations into the
ELRL, connections between expected concepts in the sentence and the word or
words that express them in the ELRL (referred to as \emph{concept-word(s)
links}), optional back-translations from the ELRL to English, and comments;
\item \textbf{Free Pairs (FPs):} additional sentence pairs from any trusted
source, optionally enriched by users with concept-word(s) links,
back-translations, and comments;
\item \textbf{Documents:} descriptive resources such as academic papers, theses,
sketches, or existing teaching materials.
\end{itemize}

This design reflects a central premise of the system: in ELRL settings, useful
structure must often be extracted from whatever evidence is available when the
target output is understood as provisional and revisable.

AEPs play a particularly important role because they are designed to collect
structural information while requiring substantially less linguistic expertise
than traditional annotation workflows. Each completed AEP contains (i) a source
sentence in English, (ii) an optional translation of the English sentence into a
lingua franca used in the field, (iii) a translation from English or the lingua
franca into the ELRL, (iv) concept-word(s) links, (v) optional
back-translations, and (vi) comments. In (iv), instead of requiring full
morphological segmentation or glossing, the system asks users to indicate which
word or words in the ELRL contribute to a given lexical or grammatical concept
assumed to be present in the sentence. This is a deliberate trade-off: detailed
annotation is costly and often unrealistic, whereas speakers and teachers can
usually indicate which word or words in the sentence contribute to lexical or
grammatical concepts. In our design, these many-to-many mappings between
concepts and forms provide enough structure to support targeted inference and
grounded draft generation while remaining far less demanding than expert
glossing.

For example, the AEP based on the sentence \textit{No, I don't cough.} asks the
language documenter to identify the word or words in the translation, if any,
that \textit{contribute} to the concepts of (i) denial; (ii) reference to the
speaker; (iii) negation; and (iv) coughing. These concepts are predefined for
each AEP. If the ELRL translation is idiomatic and does not use these concepts
directly, the language documenter is invited to enter a \emph{back-translation}:
a literal translation of the ELRL sentence into English.

We use the term \textit{language documenter} broadly to refer to any person
contributing to language description or teaching materials, including community
members, teachers, external revitalization supporters, and linguists.

In the current implementation, the AEP inventory contains 215 prompts divided
into five dialogs adapted from conversational questionnaires
\citep{francois_proposal_2019}. These prompts can typically be completed in
roughly ten hours within our workflow.

The output is a structured lesson on a user-selected topic, including
explanations, examples, and exercises. The lesson is intended to be inspected,
corrected, and adapted by teachers or speakers rather than treated as a finished
grammar. Figure~\ref{fig:pipeline} provides an overview of the pipeline.\\

The pipeline consists of seven stages:

\begin{enumerate}
\item \textbf{Data collection.} Users create or import AEPs, FPs, and documents
through the interface.
\item \textbf{Grammatical inference.} Typological priors and automated
observations extracted from AEPs are combined to infer values of grammatical
parameters supported by the available evidence. These inferred values are exposed
to the user for inspection and correction.
\item \textbf{Pseudo-glossing of AEPs.} AEPs are transformed into a linear
representation combining the source sentence, the ELRL sentence,
concept-word(s) links, and associated structural cues. This representation acts
as a lightweight substitute for expert glossing.
\item \textbf{Augmentation and indexing of FPs.} FPs are automatically enriched
with structured grammatical descriptions and cross-linguistically reusable
concepts using schema-constrained language-model calls, and can then be further
augmented by users through concept-word(s) links. These representations are
vectorized and indexed for retrieval.
\item \textbf{Processing of descriptive documents.} Documents are segmented,
embedded, and indexed so that relevant passages can be retrieved at generation
time.
\item \textbf{Query and preferences.} The user specifies a lesson topic, either
from a predefined list or through a free-form query, together with the desired
output language and expected complexity level.
\item \textbf{Aggregation and formatting.} Constrained language-model calls
combine inferred grammatical parameters, AEP pseudo-glosses, retrieved augmented
sentence pairs, and retrieved document content into a structured grammar lesson
with source traceability.
\end{enumerate}

\paragraph{Implementation note}
The current implementation is available through an online interactive interface
and does not require task-specific model training. Instead, it relies on general
large language models. This design is essential in ELRL settings, where available data are too sparse to support supervised pipelines.

\begin{figure*}[t]
\centering
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\node[data] (user) at (-3,-4) {User query\\\& Preferences};

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\node[output] (outp) at (6,-6) {Grammar lesson};

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\draw[->] (cq)--(alter);
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\draw[->] (augment)--(vecsp);
\draw[->] (docs)--(vecdoc);
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\draw[->] (vecsp)--(agg);
\draw[->] (user)--(agg);
\draw[->] (alter)--(agg);
\draw[->] (agg)--(outp);

\end{tikzpicture}
\caption{Pipeline of the system. Heterogeneous inputs are processed through inference, augmentation, and retrieval modules, then combined with the user query and generation settings to produce a structured grammar lesson. Intermediate representations remain inspectable and editable throughout the process.}
\label{fig:pipeline}
\end{figure*}

\section{System Components}

The system combines probabilistic inference, structured representations, and
retrieval-based generation. Each component is designed to produce interpretable
intermediate outputs that can be inspected and controlled by the user.

\subsection{Grammatical Parameter Inference}

The system estimates the values of grammatical parameters listed in major
typological databases such as the World Atlas of Language Structures (WALS)
\citep{wals_online} and Grambank \citep{grambank_clld_2023}. These databases
define collections of grammatical parameters and their possible values, and
record, for each language, the values of a subset of those parameters. They are
used by DIG4EL in three ways: (i) to retrieve known values for the target language when
available; (ii) to provide statistical priors over parameter values; and (iii)
to derive dependencies between values of different parameters.

Inference is performed over this network of interdependent grammatical
parameters, using typological priors together with automated observations
extracted from AEPs, following the Bayesian framing for grammatical parameter
estimation from sparse observations explored by
\citet{christian_enhancing_2025}. Rather than attempting inference over a
predetermined set of grammatical parameters, the system identifies the subset of
parameters that can be reasonably supported by the available evidence. For each
parameter, the interface exposes candidate values, confidence estimates, and the
evidence streams that contributed to the inference, allowing users to validate
or override the result.

\subsection{Sentence-Pair Representation and Augmentation}

FPs are processed into structured representations that capture grammatical and
semantic information and make them more comparable to AEPs. Each pair is
augmented with a set of grammatical descriptors (e.g., tense, aspect, polarity,
predicate type, pronouns, and clause type) and a set of cross-linguistically
reusable semantic and grammatical concepts. These representations are produced
through constrained language-model outputs.

Once sentence pairs have been augmented, users can optionally connect concepts
to the word or words that express them in the ELRL sentences, as with AEPs, and
add literal back-translations and comments. This approach avoids the need for
full morphological annotation while still providing structurally useful
information.

\subsection{Retrieval Layer}

The system uses vector-based retrieval to select relevant examples and document
content. Sentence-pair representations are embedded on the basis of their
generated grammatical descriptions rather than raw surface text alone, allowing
queries to retrieve examples on the basis of grammatical similarity instead of
direct semantic overlap. User queries are encoded using the same representation,
enabling retrieval of examples that match the intended grammatical phenomenon.

Document sources are processed separately through segmentation and embedding,
allowing relevant descriptive passages to be retrieved. This retrieval layer
provides grounded evidence for generation and allows the system to adapt
dynamically to different topics and datasets.

\subsection{Aggregation and Constrained Generation}

Based on user input, which includes the grammatical topic, the choice of output
language (limited to widely supported languages), and the desired complexity
level, a series of language-model calls aggregates information from multiple
sources, including inferred grammatical parameters, AEP pseudo-glosses,
structured representations of retrieved sentence pairs, and retrieved document
excerpts.

Generation is performed under explicit constraints: language models are
instructed to rely only on the provided inputs, to explicitly highlight conflicts
between sources, to return nothing if there is no supporting data, and to follow
a predefined lesson structure. This structure includes (i) an introduction
presenting how the grammatical topic is expressed in the chosen output language
and an overview of how it is expressed in the ELRL, with emphasis on contrasts
with the output language; (ii) a sequence of sections, each focusing on one
aspect of the topic and composed of an explanation plus examples; (iii) a
conclusion summarizing the most important points; (iv) additional examples that
can be used as exercises; and (v) a list of the sources that contributed to the
output.

\section{Interface Design Principles}

We treat usability as a first-order requirement rather than an afterthought. The
interface is designed to be usable by a broad range of language documenters and
teachers.

User interactions can be divided into two broad categories: (i) providing data
to the system and (ii) generating outputs. Data-entry workflows can tolerate
moderate complexity, whereas generation workflows must remain simple and
efficient. The interface is designed to reflect this asymmetry.

The system is organized around four design principles:

\paragraph{Accessibility}
The interface is designed to be usable by non-specialists with minimal training
across a broad range of sensory and cognitive profiles. It minimizes technical
and linguistic jargon, uses stable interaction patterns, separates more
demanding data-entry tasks from simpler generation tasks, and offers on-demand
details about processes.

\paragraph{Human-in-the-loop control}
Users retain substantial control over inference and generation. They can inspect
and edit inferred grammatical properties, include or exclude sources, and revise
generated outputs directly. Outputs are provided in forms that can be further
edited and adapted for pedagogical use.

\paragraph{Explainability through decomposition}
The system maintains a separation between data sources and processing stages.
Each source contributes independently to the final output, allowing users to
trace generated content back to its origin.

\paragraph{Progressive data enrichment}
The system supports incremental improvement as new data become available.
Outputs can be regenerated with updated inputs without retraining or
reconfiguration, enabling iterative refinement of both data and pedagogical
materials. A typical example is a teacher adding a new set of sentence pairs for
a forthcoming lesson, which the system can incorporate into later reasoning and
generation.

\section{User Workflows}

The user workflows are designed to reduce the same two bottlenecks as the system
itself: dependence on expert annotation at input time and dependence on
linguist-authored grammars at output time. Accordingly, the interface separates
workflows for making data available to the system from workflows for generating
and revising lessons. Neither workflow requires formal training in linguistics.

Interaction is organized into three main workflows: (i) data entry and
augmentation, (ii) grammatical parameter inference and validation, and (iii)
lesson generation and revision.

\subsection{Data Entry and Augmentation}

The first workflow is designed for language documenters. Its purpose is to
collect enough structured evidence for downstream inference and lesson
generation.

Data entry is organized into three modules corresponding to the supported input
types. All uploaded or created resources must include user-provided metadata,
including provenance, licensing or reuse conditions, and confirmation that the
data were collected with appropriate consent and may be processed by the system
under the license governing its outputs.\footnote{In the current implementation,
resources retain their original license when one exists. Material created within
the system is governed by the Creative Commons Attribution--NonCommercial--
ShareAlike 4.0 license.}

\paragraph{Annotated Elicited Pairs (AEPs).}
AEPs are the most structured input modality. Users translate a predefined set of
elicitation prompts into the target language and add as many concept-word(s)
links, back-translations in English or in a lingua franca, and comments as
possible, as described in Section~\ref{overview}.

The interface supports both online entry and offline completion through
downloadable spreadsheet templates that can later be re-imported. This hybrid
design is important in practice, as many users work with intermittent
connectivity or prefer offline data collection. Uploaded AEPs remain editable,
allowing users to refine and enrich them over time. An example of the AEP
interface is presented in Appendix~\ref{aep_interface}.

\paragraph{Free Pairs (FPs).}
Users may also upload arbitrary collections of sentence pairs, using the
provided spreadsheet template or exports from established corpus-collection
software. These pairs are less constrained than AEPs and are intended to capture
any available material.

Once uploaded, the user triggers their augmentation with structured grammatical
descriptions and cross-linguistically reusable concepts by schema-constrained
language models. This augmentation step is computationally heavier than standard
data entry and is therefore handled as a long-running server-side process, with
progress information displayed in the interface. After processing, users may
enrich some pairs with concept-word(s) links, back-translations, and comments.
In this way, FPs progressively move from raw examples toward structurally useful
evidence.

\paragraph{Documents.}
Users can upload descriptive documents relevant to the ELRL, including academic
papers, theses, sketches, or existing pedagogical materials. Once metadata have
been entered, documents are segmented, embedded, and indexed automatically.
These resources then become available for retrieval during lesson generation.

Taken together, these three input modalities allow the system to operate under a
wide variety of data conditions.

\subsection{Inference and Validation}

The second workflow makes inferences about grammatical parameters visible and
editable. This is central to the human-in-the-loop design: the system does not
treat inferred structure as hidden internal state, but as provisional material
that users can inspect and revise.

The system identifies a collection of grammatical parameters whose values can be
robustly inferred. It infers those values and exposes them through the interface
with optional supporting evidence. Users can review inferred grammatical
properties and modify any inferred value accordingly. An example of this
interface is provided in Appendix~\ref{gp_feedback}.

\subsection{Lesson Generation and Revision}

The third workflow is designed to be as straightforward as possible. The
interface minimizes complexity and focuses on a small number of decisions
directly relevant to lesson creation. Appendix~\ref{gen_menu} shows a screenshot
of the generation interface.

Users specify a grammar topic, either by selecting a predefined topic or by
entering a free-form query. They also choose the output language and the
intended audience level. Based on these preferences, the system aggregates
inferred grammatical parameters, pseudo-glossed elicited pairs, augmented
sentence pairs, and retrieved document content into a structured lesson draft.
Predefined grammar topics also benefit from response templates designed by
linguists.

The generated output is intentionally framed as editable pedagogical material
rather than as a definitive grammar. The interface also preserves traceability
to the sources that contributed to the output, allowing users to inspect the
basis of the generated content. When the system detects conflicts across sources
or the absence of supporting evidence, it is designed to expose them explicitly
in the output so that they can be resolved by the user.

This workflow operationalizes the core claim of the paper: teachers do not need
to wait for a linguist-authored pedagogical grammar before they can obtain
usable teaching material. With an appropriate workflow and interface,
non-specialists can provide enough structure to support first-draft generation
in at least some ELRL settings, after which teachers can adapt the output to
their classroom needs.

\section{Example}
\label{sec:example}

This section illustrates the workflow through the generation of a lesson on
negation in Tahitian\footnote{Tahitian is used here because all resources are
public.}. The available sources for this example include 215 Annotated Elicited
Pairs (AEPs), collected in approximately ten hours; 3,000 Free Pairs (FPs)
gathered from multiple sources; and one descriptive document: a French academic
document covering selected topics in Tahitian grammar.

Tahitian is partially documented in the typological databases WALS and
Grambank. When the grammatical-parameter inference module was run, 37 inferred values were presented to the user who was invited to either accepting them, changing
them, or removing the corresponding parameter when uncertain. 

The FPs were automatically augmented and indexed by the system. In the current
configuration, this preprocessing step took approximately one day for the 3,000
sentence pairs. 

The descriptive document was segmented, vectorized, and made
available for retrieval within a few minutes after upload. These preprocessing
steps are performed only once and are re-run only when the corresponding sources
are modified.

On the \emph{grammar generation} page, English was selected as the output
language and \emph{teenagers} as the intended audience level. Although the
document is intended for teachers, the \emph{teenagers} selection is often used as it removes most linguistic jargon from the output. The predefined topic \emph{Negation} was
chosen. The generation interface is shown in Appendix~\ref{gen_menu}.

Because of the use of large language models, outputs are not
identical across runs. However, repeated generations for the same query and
source configuration consistently include the same core grammatical information.
The generated lesson begins with an introduction that contrasts negation in the
output language, here English, with negation in Tahitian. One generated
introduction is reproduced below.

\begin{quote}
Negation lets us say that something is not so, does not exist, will not happen,
or must not be done. In English you usually add words like \emph{not},
\emph{no}, \emph{don't}, \emph{won't}, \emph{isn't}, \emph{never}, or
\emph{no longer}. English tends to keep the sentence shape and add
\emph{not} to the helper verb, or uses \emph{no} for existence
(\emph{there is no water}), or uses \emph{don't}/\emph{won't} for commands and
future. In Tahitian, the choice of the negative word depends on the kind of
statement being made: actions that already happened or are viewed as facts,
future events and abilities, equative ``X is Y'' sentences,
existence/possession, and prohibitions each take a different dedicated negator.
You will see four core forms throughout this lesson: \emph{'aita} for factual
events and for existence/possession, also as a free-standing ``no'';
\emph{e'ita}/\emph{'eita} for future and ability; \emph{e'ere} for ``is not''
classifications; and \emph{'eiaha} for ``don't.'' Other useful pieces include
\emph{'ore} in certain subordinate clauses, the pattern with \emph{i} before the
verb after \emph{'aita}, the use of \emph{e} before the verb after
\emph{e'ita}/\emph{'eita} and \emph{'eiaha}, and adverbs such as
\emph{a'e nei} ``ever/never,'' \emph{fa'ahou} ``any more/again,'' and
\emph{\={a}} ``still/not yet'' under negation. Where English uses one general
\emph{not}, Tahitian selects the appropriate negative marker for the clause type
and places it at the front of the clause.
\end{quote}

The lesson then presents a sequence of sections covering different types of
negation in Tahitian. In this generation, the first three sections after the
introduction are: (i) \emph{How Tahitian negation flips the sentence};
(ii) \emph{'Aita: general ``not,'' past/experienced actions, and ``there is no
...''}; and (iii) \emph{E'ita: future/irrealis and general ``won't / wouldn't /
can't''}.

Each section contains examples retrieved from the corpus together with short explanations of their grammatical relevance. For example, the first section includes the following
item:

\begin{quote}
\textbf{1) \emph{'aita, 'aita e 'ōro'a}}\\
\emph{No, there is no celebration.}\\
The pattern \emph{'aita e NP} means ``there is no NP.''
\end{quote}

Three complete generations on negation in Tahitian are available for download at \url{https://zenodo.org/records/20045485}.

\section{Initial Deployment Experience}

This section reports initial deployment experience and qualitative observations
rather than a controlled evaluation of pedagogical effectiveness. DIG4EL is
currently used with 24 typologically diverse languages, ranging from North
Africa to Papua New Guinea. These include relatively well-documented languages,
such as Mwotlap in Vanuatu, as well as minimally documented languages that test
the limits of the system, such as Bwatoo in New Caledonia.

\subsection{Workshop use by non-specialists}

One illustrative deployment was a workshop held at Vanuatu National University.
Vanuatu is home to more than one hundred languages. Participants came from
diverse professional backgrounds and were all involved in supporting local
languages. For many of them, this was their first experience documenting their
own language without direct assistance from a linguist.

During the workshop, participants entered AEPs, created concept-word(s) links,
and generated lesson content. Not every participant completed the full workflow
in their own language during the session, largely because of time constraints,
but the workshop showed that non-specialist users could learn and operate the
core components after an initial introduction. The claim here is intentionally
modest: the lightweight input workflow and the lesson-generation workflow were
usable under workshop conditions. The workshop also suggested that participants
valued documenting their own language directly and responded positively to
generating and revising content. One outcome of this workshop was a formal
cooperation agreement and deployment program that will pilot DIG4EL in a group
of schools over the next two years.

\subsection{Use within ongoing documentation work}

Beyond workshop settings, the system has also been adopted by the CNRS Heliceo
project, which aims to document a large number of Pacific languages and
modernize parts of the documentation process.

In this context, the system is used not only as a generation tool, but also as a
way to structure data entry, lightweight annotation, and the progressive
enrichment of language resources by a broad range of language documenters.

This second deployment matters because the system is being used as
infrastructure for iterative data collection and reuse: AEPs, FPs, and uploaded
documents can be expanded over time, reprocessed, and fed back into later lesson
generation.

Taken together, these deployments support a bounded but important conclusion:
the workflow is operational outside a laboratory setting and can support both
first-draft lesson generation and incremental resource building.

\section{Discussion}

The system reflects a shift in how NLP can be applied to ELRLs. In such settings, the central challenge is often not maximizing benchmark performance, but enabling useful work to happen despite the absence of expert annotation, large corpora, and sufficient linguistic labor. The contribution of the system is therefore not to eliminate uncertainty, but to make progress possible in such documentation and teaching process.

More specifically, the system is designed to mitigate two long-standing bottlenecks. First, it reduces dependence on expert linguistic annotation by replacing glossing- or treebank-centered workflows with lightweight concept annotation that most speakers can provide. Second, it reduces dependence on linguist-authored pedagogical grammars by generating structured lesson drafts that teachers and speakers can revise.

Several broader observations follow from this design. First, grammar teaching material can be initiated by a broader range of language practitioners than is usually assumed in NLP pipelines, provided that the workflow and interface are designed accordingly. In ELRL contexts, shifting data collection and content creation toward speakers and community-based practitioners, rather than positioning them primarily as assistants to external experts, is a meaningful practical and ethical change. Second, combining heterogeneous sources of evidence improves robustness under extreme data scarcity. Third, transparency is not merely a usability feature, but a methodological requirement in contexts where uncertainty is unavoidable. Users must be able to inspect intermediate inferences, identify implausible outputs, and decide which sources to trust. Finally, human oversight remains essential. The system is useful precisely because it supports revision, not because it removes the need for it.

\paragraph{Risks and Limitations.}
The same design choices that make the system usable under extreme scarcity also introduce risks and limitations.

\begin{itemize}
\item \textbf{Error propagation.} Generated outputs may be difficult for teachers to correct, especially when they appear authoritative or contain technical linguistic terms. This creates a risk that errors may be propagated rather than identified.

\item \textbf{Reuse conditions.} The current implementation assumes that input materials can be processed under declared reuse conditions. This restricts applicability in contexts where communities, authors, or institutions impose cultural, ethical, or legal constraints on access and reuse.

\item \textbf{Institutional barriers.} The system lowers technical and linguistic barriers, but it does not resolve social and institutional ones. Some teachers may still feel that they are not legitimate producers of explicit grammar lessons even when appropriate tools are available.

\item \textbf{Usable evidence and willingness to revise.} The system's usefulness depends on the availability of at least some usable evidence related to the grammar topic expressed in the query, on the relevance of retrieved materials, and on the willingness and ability of users to revise outputs.
\end{itemize}

\section{Ethical Considerations}

\paragraph{Consent, rights, and governance}
Language data and descriptive materials may be culturally sensitive, collectively governed, or subject to local restrictions that go beyond standard copyright. The system is therefore designed to require metadata about provenance, consent, and reuse conditions at upload time. The intent is to observe CARE first, then FAIR principles, which may diverge in practice depending on the situation, but no interface can guarantee that user-provided declarations fully capture local governance norms or community expectations.

\paragraph{Representation of variation}
Endangered languages often display dialectal variation, intra-community disagreement, or incomplete standardization. Any system that aggregates across examples and documents risks overrepresenting one variety, suppressing variation, or presenting contingent analyses as settled facts. This risk cannot be fully removed and is managed in the system by allowing users to specify the relevant language variety, if any.

\paragraph{Access and dependency}
The current implementation depends partly on online services and on data that can be processed under declared reuse conditions. These assumptions may exclude communities with limited connectivity or stricter cultural and legal controls over language materials. Future work should therefore prioritize offline deployment and more flexible governance mechanisms.

\section{Conclusion and Future Work}

We presented a human-in-the-loop system for generating revisable grammar lessons for extremely low-resource languages from minimal and heterogeneous data. By combining lightweight concept-based annotation, typological inference, structured augmentation, retrieval, and constrained generation, the system makes it possible to produce first pedagogical drafts without requiring expert annotation as a prerequisite.

The contribution of the paper is an implemented workflow and a deployed system design for early pedagogical draft generation under severe data scarcity. Initial qualitative feedback from multilingual communities and language documenters has been positive, although this approach also introduces its own technical, ethical, and social risks and limitations.

Future work includes controlled evaluation of lesson quality and usability, broader offline deployment, and deeper integration into educational and community-led documentation settings.

\section*{Acknowledgments}

This work was supported by the French CNRS Heliceo project under grant
ANR-24-RRII-001.\\

We are deeply grateful to the language documenters and community members with
whom we have had the opportunity to work. Their patience, feedback, and support
have been essential in making the system possible and in adapting it to practical
language documentation and teaching contexts.\\

We also thank the Government of Vanuatu and its partner institutions for their
willingness to support the first school-based deployment of DIG4EL.

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\appendix

\section{AEP Augmentation Interface}
\label{sec:aep_interface}

\begin{center}
    \includegraphics[width=\linewidth]{aep_example.png}
    \captionof{figure}{AEP interface showing data in Mwotlap, a language from Vanuatu. The sentence in English is at the top, with no lingua franca used. The right column helps the user keep the context in mind by showing the portion of the dialog surrounding the current sentence. Below the translation, each concept is listed with a box allowing the user to select none, one, or multiple words from the translation for each expected elicited concept. Below this, a text box invites the user to enter the literal back-translation, if relevant, followed by comments.}
    \label{aep_interface}
\end{center}

\section{Grammatical Parameters Inference Feedback Interface}
\label{sec:gp_feedback}

\begin{center}
    \includegraphics[width=\linewidth]{gp_feedback.png}
    \captionof{figure}{Interface showing the result of the grammatical-parameter inference process. At the top are the parameter-discovery results. Then, for each parameter, the interface displays the value currently inferred for the language, together with the confidence derived from the entropy of the distribution. The user can flip the \textit{Let me edit beliefs} switch, which allows any value to be corrected before further processing.}
    \label{gp_feedback}
\end{center}

\section{Grammar Lesson Generation Menu}
\label{gen_menu}

\begin{center}
    \includegraphics[width=\linewidth]{gen_menu.png}
    \captionof{figure}{Interface used to generate grammatical descriptions. Outputs stored online can be directly displayed or downloaded; Outputs are not stored by default, the user makes the decision. The generation menu includes a selection of the format, the output language, and the complexity modeled as age brackets. The user then selects a pre-defined grammar topic or enter a free query before launching the generation.}
    \label{gen_menu}
\end{center}

\end{document}