Evidence map›Paper›PMID 38778968›Full record

ReviewHeliyon2024

Investigating the integration of artificial intelligence in English as foreign language classes for enhancing learners' affective factors: A systematic review.

Lujain AlTwijri, Talal Musaed Alghizzi

Abstract readReview
In one paragraph

Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lujain AlTwijriImam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.
Talal Musaed AlghizziImam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrating Artificial Intelligence (AI) applications into language learning and teaching is currently a growing trend in higher education. Literature reviews have demonstrated the effectiveness of AI applications in improving English as a foreign language (EFL) and English as a second language (ESL) learners' receptive and productive skills, vocabulary knowledge, and intercultural competencies. However, systematic reviews investigating the usefulness of AI technologies in higher education to enhance EFL learners' affective factors are scarce. This study is a systematic review that investigates the effectiveness of integrating AI technologies to enhance EFL learners' motivation, engagement, and attitude, and reduce their learning anxiety. Articles from reputable journal databases such as IEEE, Wiley, Web of Science, Sage, ProQuest, Springer, and Science Direct were screened by examining titles and abstracts, and irrelevant articles were excluded from the search. Of the 64 articles analyzed only 21 articles published between 2017 and 2023 were determined to be relevant to the research topic. The findings suggest that the implementation of AI technologies in EFL contexts is in its early stages, and further research is required to establish the impact of AI-integrated classes on EFL learners' affective factors. This review also identifies the gaps in literature and recommends avenues for future research in this novel area.

Indexed as

Affective factorsArtificial intelligenceEnglish as a foreign languageHigher education

Identifiers

PMID38778968
PMCPMC11109823

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.