CoChat
Sign Up
Cited by 145Second Language Acquisition and LearningArtificial Intelligence in EducationEFL/ESL Teaching and Learning

The DOI matches its Crossref record; 46 of the 49 references it cites were found in a registry or an index, and 3 are not checked yet.

The impact of chatbots based on large language models on second language vocabulary acquisition

Open access
Zhang, Zhihui, Huang, Xiaomeng·2024·Heliyon
DOI 10.1016/j.heliyon.2024.e25370PMC PMC10850600
Open this paper in CoChat — free account

Abstract

In recent years, the integration of artificial intelligence (AI) and machine learning (ML) into education, particularly for Personalized Language Learning (PLL), has garnered significant attention. This approach tailors interventions to address the unique challenges faced by individual learners. Large Language Models (LLMs), including Chatbots, have demonstrated a substantial potential in automating and enhancing educational tasks, effectively capturing the complexity and diversity of human language. In this study, 52 foreign language students were randomly divided into two groups: one with the assistance of a Chatbot based on LLMs and one without. Both groups learned the same series of target words over eight weeks. Post-treatment assessments, including systematic observation and quantitative tests assessing both receptive and productive vocabulary knowledge, were conducted immediately after the study and again two weeks later. The findings demonstrate that employing an AI Chatbot based on LLMs significantly aids students in acquiring both receptive and productive vocabulary knowledge during their second language learning journey. Notably, Chatbots contribute to the long-term retention of productive vocabulary and facilitate incidental vocabulary learning. This study offers valuable insights into the practical benefits of LLM-based tools in language learning, with a specific emphasis on vocabulary development. Chatbots utilizing LLMs emerge as effective language learning aids. It emphasizes the importance of educators understanding the potential of these technologies in L2 vocabulary instruction and encourages the adoption of strategic teaching methods incorporating such tools.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.heliyon.2024.e25370

Cites

46 of 49 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 The transformer architecture.
Fig. 1 The transformer architecture.CC BY · PubMed Central
Fig. 2 The LLMs for specific purposes.
Fig. 2 The LLMs for specific purposes.CC BY · PubMed Central
Fig. 3 RLHF and proximal policy optimization (PPO).
Fig. 3 RLHF and proximal policy optimization (PPO).CC BY · PubMed Central
Fig. 4 For the research experiment design.
Fig. 4 For the research experiment design.CC BY · PubMed Central
Fig. 5 The system architecture of AI chatbot.
Fig. 5 The system architecture of AI chatbot.CC BY · PubMed Central
Fig. 6 The prototype for chatbots (LLMs).
Fig. 6 The prototype for chatbots (LLMs).CC BY · PubMed Central
Fig. 7 Descriptive statistical results of receptive and productive words ((a) the results of paired sample tests; (b) the independent sample).
Fig. 7 Descriptive statistical results of receptive and productive words ((a) the results of paired sample tests; (b) the independent sample).CC BY · PubMed Central
Fig. 8 Delay test for receptive and productive vocabulary (EG and CG results) ((a) the test result of paired samples; (b) the independent sample).
Fig. 8 Delay test for receptive and productive vocabulary (EG and CG results) ((a) the test result of paired samples; (b) the independent sample).CC BY · PubMed Central
Fig. 9 The frequency of using the chatbot (LLMs) in eight weeks.
Fig. 9 The frequency of using the chatbot (LLMs) in eight weeks.CC BY · PubMed Central
Fig. 10 Distribution of questions from eight-week students in chatbot (LLMs).
Fig. 10 Distribution of questions from eight-week students in chatbot (LLMs).CC BY · PubMed Central
Fig. 11 Distribution of question types from eight-week students in chatbot (LLMs).
Fig. 11 Distribution of question types from eight-week students in chatbot (LLMs).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 46 references

Research works printed but not linked: 1

  1. Nation, 2001, vol. 10

Cited by

Sorted by citations

Items 101–145 of 145

Related

See all

Metadata

Authors
Zhang, Z., & Huang, X.
Affiliations
  • University of Southern California (US)
  • Alibaba Group (China) (CN)
Corresponding author
Zhang, Zhihui
ORCID
Venue
Heliyon
Publisher
Elsevier BV
Published
2024-02
Volume
10
Issue
3
Pages
e25370
ISSN
2405-8440 (ISSN-L)
DOI
10.1016/j.heliyon.2024.e25370
PMC
PMC10850600
OpenAlex
W4391429431
Cited by
107
References
46
Citations by year
2026: 44 · 2025: 49 · 2024: 14
FWCI
83.49
Type
journal article
Language
en
Open access
Licence
Locations
  • Heliyon (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Second Language Acquisition and Learning
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
Keywords
chatbots, large language models, second language vocabulary acquisition, personalized language learning, receptive vocabulary knowledge, productive vocabulary knowledge, incidental vocabulary learning, vocabulary retention, foreign language learners, AI-assisted language learning

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 ›

Are you an author, or do you see a problem with this page? Request a review