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Cited by 285EFL/ESL Teaching and LearningEnglish Language Learning and TeachingArtificial Intelligence in Education

Partnering with AI: Intelligent writing assistance and instructed language learning

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Godwin-Jones, Robert·2022·Language Learning & Technology
DOI 10.64152/10125/73474
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Abstract

In recent years, advances in artificial intelligence (AI) have led to significantly improved, or in some cases, completely new digital tools for writing. Systems for writing assessment and assistance based on automated writing evaluation (AWE) have been available for some time. That is the case for machine translation as well. More recent are synchronous feedback tools, such as Grammarly. That tool incorporates, as do others, predictive text technology, supplying automated sentence completion. Emerging writing assistance goes further, generating an entire text in response to a brief prompt. That capacity, along with significantly improved performance of both automated feedback systems and machine translation, is enabled through advances in AI, built on ever larger datasets and deep machine learning. While they differ in interface, functionality, and target audience, the available and emerging set of intelligent writing tools can be used to help learners improve the quality of their written texts. However, their use in instructional language learning has in some cases been controversial. In this column, we will be examining AI-enabled writing tools, reviewing the findings from research studies, and discussing their use in instructional settings. When integrated into writing instruction and practice, these digital tools have been found to offer significant benefits to both students and teachers. Teacher mediation aids learners in becoming informed consumers of language technology, as well as helping them to gain meta-linguistic knowledge. For researchers, intelligent writing tool use is optimally analyzed from a broad ecological perspective that examines the dynamic interplay of learner, software, and instructional environment.

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  • Use of LLMs in AWE tools can reduce the burden on EFL instructors.

    “the use of LLMs in these tools can particularly reduce the burden on EFL instructors (Godwin-Jones, 2022).”

    From Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond (2023) · cites this paper as Godwin-Jones, 2022

    The Ecological Perspectives section (page unavailable) says AWE can free teachers’ time from correcting language mechanics so they can address higher-order writing issues. The Introduction describes large language models as enabling language services, but the paper does not specifically say that LLMs in AWE tools reduce instructors’ workload, or that the benefit is particular to EFL instructors. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Authors
Godwin-Jones, R.
Venue
Language Learning & Technology
Publisher
National Foreign Language Resource Center
Published
2022-06-10
Volume
26
Issue
2
Pages
5-24
ISSN
1094-3501 (ISSN-L)
DOI
10.64152/10125/73474
OpenAlex
W4413183450
Cited by
285
References
0
Citations by year
2026: 137 · 2025: 92 · 2024: 35 · 2023: 20 · 2022: 1
FWCI
112.92
Type
journal article
Language
en
Open access
Licence
Locations
  • Language learning & technology (publishedVersion, cc-by-nc-nd)
  • ScholarSpace (University of Hawaii at Manoa) (submittedVersion, cc-by-nc-nd)
Topics
  • Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
  • Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
AI writing tools, automated writing evaluation, writing instruction, teacher mediation, metalinguistic knowledge, machine translation, text prediction, synchronous feedback, text generation, ecological perspective

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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