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# Towards Fine-Grained Pedagogical Control over English Grammar Complexity in Educational Text Generation

**Authors:** Dominik Glandorf and Detmar Meurers · **Year:** 2024 · **Venue:** Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024) **Link:** [aclanthology.org/2024.bea-1.24](https://aclanthology.org/2024.bea-1.24/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**0** citations](https://openalex.org/works?filter=cites:W6891810003) openalex-snapshot · 2026-10-14[**7** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/glandorf-2024-fine-grained-pedagogical-control-over/) of the 140673 papers whose bibliographies we read[Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)](https://aclanthology.org/volumes/2024.bea-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2024.bea-1.24/)
- [PDF](https://aclanthology.org/2024.bea-1.24.pdf)
- [DOI 10.48448/nr2j-b002](https://doi.org/10.48448/nr2j-b002)
- [OpenAlex](https://openalex.org/W6891810003)

**Authors (2)**

1. [Dominik Glandorf](https://openalex.org/A5052977581) [ORCID](https://orcid.org/0009-0000-0038-2721) · [Semantic Scholar](https://www.semanticscholar.org/author/2308475852) · h 2 · 28 citations · 19 works
2. [Detmar Meurers](https://openalex.org/A5000361724) [ORCID](https://orcid.org/0000-0002-9740-7442) · [Semantic Scholar](https://www.semanticscholar.org/author/1831176) · h 36 · 4114 citations · 351 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

The paper asks whether language models can control English grammar for learners. It takes the 1,222 patterns of the English Grammar Profile, each placed at a CEFR level. GPT-3.5 and GPT-4 wrote positive examples and minimal-pair negatives for each pattern; a manual check found 87.1% (GPT-3.5) and 92.9% (GPT-4) correct instances. The authors used these to label 946,246 sentences and trained one small BERT-based detector per CEFR level, with one output head per pattern; average accuracy is 95.1%. Finally, Mistral-7B generates five candidate sentences per prompt and keeps the one with the most patterns of the target level. For C1 the share of such patterns rises from 5.1% in a static CEFR-labelled corpus to 20.6%, above the 14.2% of GPT-4 prompting.

## Ideas

- I1. Rule-based detectors: write each pattern as a pattern over UD trees (for example past perfect as an auxiliary *had* over a past participle) and count matches. Each detection can name the arcs that matched, which a neural detector cannot do.
- I2. Minimal pairs by rule: derive the negative of a pattern by removing its construction (e.g. *had expected* to *expected*) and check it with the Mova parser. This gives a labelled set without any model calls.
- I3. Candidate ranking in the realiser: generate several candidates from `en/src/lin.rs` and keep the one with the most matches at the target level. Negative control: keep a random candidate; the level count must not rise.
- I4. The paper finds the generated instances too homogeneous. Measure the same for the Mova candidate set, before any claim of control.

## For Mova

Chapters: [13. Verbs](https://md.mova.elamur.ai/free/en/docs/languages/en/13-verbs/) (tense and aspect), [11. Nouns](https://md.mova.elamur.ai/free/en/docs/languages/en/11-nouns/) (articles and bare plurals), [25. Relative clauses](https://md.mova.elamur.ai/free/en/docs/languages/en/25-relative-clauses/). Code: `en/src/lin.rs` for candidates, `en/src/udreg.rs` for the features to match. Measure: share of target-level patterns per generated text on held-out lessons (not the release test), with the random-candidate control; two runs must be identical.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.48448/nr2j-b002](https://doi.org/10.48448/nr2j-b002)

```
@inproceedings{glandorf2024fine,
  title = {{Towards Fine-Grained Pedagogical Control over English Grammar Complexity in Educational Text Generation}},
  author = {Glandorf, Dominik and Meurers, Detmar},
  booktitle = {Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)},
  year = {2024},
  doi = {10.48448/nr2j-b002},
  url = {https://doi.org/10.48448/nr2j-b002}
}
```

- [← previous Towards Pragmatic Production Strategies for Natural Language Generation Tasks](https://md.mova.elamur.ai/free/en/docs/papers/giulianelli-2022-pragmatic-production-strategies-natural-language/)
- [next → Breaking NLI Systems with Sentences that Require Simple Lexical Inferences](https://md.mova.elamur.ai/free/en/docs/papers/glockner-2018-breaking-nli-systems-sentences-that/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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