Responsible and Ethical Use of Artificial Intelligence in Language Education: A Systematic Review

Abstract

A plethora of publications have shed light, particularly on the affordances of artificial intelligence (AI) in language education, garnering significant attention, promising transformative impacts on teaching and learning practices. However, the rapid adoption of AI tools has raised ethical concerns regarding data privacy, bias and academic integrity. in response to these concerns, this systematic review aims to explore the responsible and ethical use of AI in language education (REALE) by examining recent literature from 2020 to 2024. The structure of this research revolves around two key questions: What are the emerging patterns and practices in REALE? and What research methodologies have been utilized in studies examining REALE? The researchers selected 9 studies from 65 publications in the Web of Science (WoS) and Scopus databases, following a rigorous screening process based on predefined inclusion and exclusion criteria. These selected studies were analyzed using thematic codes: the objective of the study, methodologies applied, sample, country and the-NonCommercial 4.0 International (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/). 316 Forum for Linguistic Studies | Volume 06 | Issue 05 | November 2024 key outcomes reported. The findings reveal a growing trend towards implementing AI in language education, with an emphasis on ethical training and awareness. The review suggests the necessity for educators and policymakers to develop comprehensive guidelines for the responsible and ethical use of AI in language education. It also recommends further research into inclusive and ethical AI practices across different educational levels to foster a more equitable and responsible use of technology in language education.

Full text

Forum for Linguistic Studies | Volume 06 | Issue 05 | November 2024
Forum for Linguistic Studies
https://journals.bilpubgroup.com/index.php/fls
REVIEW
Responsible and Ethical Use of Artificial Intelligence in Language
Education: A Systematic Review
Nurkhamimi Zainuddin 1* , Nur Azlin Suhaimi 1, Mohammad Najib Jaffar 1, Norita Md Norwawi 2,
Muhammad Sabri Sahrir 3, Wan Ab Aziz Wan Daud 4, Mohammad Taufiq Abdul Ghani 5
1
Faculty of Major Language Studies, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan,
Malaysia
2
Faculty of Science and Technology, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan,
Malaysia
3Kuliyyah of Education, International Islamic University Malaysia, 53100 Gombak, Selangor, Malaysia
4
Faculty of Language Studies and Human Development, Universiti Malaysia Kelantan, 16300 Bachok, Kelantan, Malaysia
5
Faculty of Languages and Communication, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia
ABSTRACT
A plethora of publications have shed light, particularly on the affordances of artificial intelligence (AI) in language
education, garnering significant attention, promising transformative impacts on teaching and learning practices. However,
the rapid adoption of AI tools has raised ethical concerns regarding data privacy, bias and academic integrity. in response to
these concerns, this systematic review aims to explore the responsible and ethical use of AI in language education (REALE)
by examining recent literature from 2020 to 2024. The structure of this research revolves around two key questions:
What are the emerging patterns and practices in REALE? and What research methodologies have been utilized in studies
examining REALE? The researchers selected 9 studies from 65 publications in the Web of Science (WoS) and Scopus
databases, following a rigorous screening process based on predefined inclusion and exclusion criteria. These selected
studies were analyzed using thematic codes: the objective of the study, methodologies applied, sample, country and the
*CORRESPONDING AUTHOR:
Nurkhamimi Zainuddin, Faculty of Major Language Studies, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan,
Malaysia; Email: khamimi@usim.edu.my
ARTICLE INFO
Received: 20 August 2024 | Revised: 28 August 2024 |Accepted: 9 September 2024 | Published Online: 8 November 2024
DOI: https://doi.org/10.30564/fls.v6i5.7092
CITATION
Zainuddin, N., Suhaimi, N.A., Jaffar, M.N., et al., 2024. Responsible and Ethical Use of Artificial Intelligence in Language Education: A Systematic
Review. Forum for Linguistic Studies. 6(5): 316–325. DOI: https://doi.org/10.30564/fls.v6i5.7092
COPYRIGHT
Copyright © 2024 by the author(s). Published by Bilingual Publishing Co. This is an open access article under the Creative Commons Attribution-
NonCommercial 4.0 International (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/).
316

Forum for Linguistic Studies | Volume 06 | Issue 05 | November 2024
key outcomes reported. The findings reveal a growing trend towards implementing AI in language education, with an
emphasis on ethical training and awareness. The review suggests the necessity for educators and policymakers to develop
comprehensive guidelines for the responsible and ethical use of AI in language education. It also recommends further
research into inclusive and ethical AI practices across different educational levels to foster a more equitable and responsible
use of technology in language education.
Keywords: Artificial Intelligence; Systematic Review; Language Education; Responsible; Ethics
1. Introduction
Prior to the advent of Artificial Intelligence (AI) tech-
nology, Information and Communication Technology (ICT)
had significantly contributed to enhancing learning settings
through various advancements and transformations. Accord-
ing to Papadakis, the integration of IoT, AI and ICT has
further amplified these transformations, enabling more ac-
cessible and inclusive education
[1]
. Subsequently, AI has
garnered the attention of education experts in several ways,
particularly in its influence on the methods and practices
of teaching and learning
[2–4]
. AI technology is becoming
more and more interwoven into several elements of life in
this era characterised by increased mobility and technology.
Aravantinos et al. highlight the growing presence of AI in pri-
mary school settings, underscoring the importance of under-
standing its educational impact through systematic reviews
of existing literature[5]. Prior research has observed that AI
technology has been extensively incorporated into educa-
tion through the utilisation of natural language processing in
machine learning, data mining and learning analytics[2, 4, 6].
The application of AI in education is growing signifi-
cantly
[7–9]
. AI is an educational tool that allows educators to
provide content with greater efficiency and significance. AI
is not solely a learning tool; rather, it possesses the capability
to comprehend and address the unique requirements of stu-
dents, fostering a more engaging and immersive learning at-
mosphere. Lavidas et al. further emphasize that AI’s role ex-
tends beyond the classroom, influencing students’ intentions
to use AI applications for academic purposes, particularly
in the humanities and social sciences
[10]
. AI has numerous
applications that advance education in a more progressive
manner. These include the utilisation of an Intelligent Tutor-
ing System, Voice Assistant, Personalised Learning, Virtual
Mentor, Smart Content, Automatic Assessment and Educa-
tional Games. Education integrates humans with technology
through the utilisation of various technologies, resulting in a
flexible and participatory learning method[11].
2. Literature Review
2.1.
Artificial Intelligence in Language Educa-
tion
The use of AI in language education has many benefits,
including the ability to tailor lessons to the needs of different
students, giving students immediate, personalized feedback
on their work, creating effective tests and forecasting stu-
dent performance in the classroom
[12–15]
. AI in language
education guarantees optimal support for students through-
out their studies
[16, 17]
. Students may progress through their
coursework at their own pace, receive immediate feedback
on their development and be guided without the necessity for
direct teacher intervention
[18]
. It can provide students with
learning experiences tailored to their individual needs, pro-
vide revision suggestions and measure their progress
[19, 20]
.
AI provides a new foundation for educators to construct
an adaptive and individualized language classroom
[21]
. AI-
based solutions can ease educators’ burdens in several ways,
including the use of facial recognition for attendance, au-
tomatic evaluation of students, correction of pronunciation,
monitoring and recording of student emotions and behav-
iors, collecting resources, marking homework and answering
student questions[22–25].
Regarding language teaching, a few studies have high-
lighted the efficacy of AI in assisting students with vocabu-
lary acquisition, pronunciation and the development of all
four language abilities. Attention was focused on many
learner-related matters, including their level of attentiveness,
level of engagement, level of interest and attitude, as well as
the assessment of their competency and level of achievement.
The results encompassed enhanced writing proficiency, pre-
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Forum for Linguistic Studies | Volume 06 | Issue 05 | November 2024
cision, constructive discourse, diminished speech-related
worries and a heightened level of involvement[26–29].
Recent studies have also highlighted how AI-driven
chatbots significantly enhance academic engagement among
EFL students by fostering deeper learning interactions and
promoting more active participation
[30]
. Wu et al. have ex-
plored the determinants influencing EFL learners’ intentions
to use AI in distributed learning environments, emphasiz-
ing the importance of understanding learners’ perceptions
and the contextual factors that drive AI adoption
[31]
. More-
over, the engagement of Chinese EFL learners with large
language models has been investigated, revealing the critical
role of autonomy, competence and relatedness in fostering
effective learning outcomes
[32]
. Research into vocabulary
learning using large language models shows that AI tools
offer unique advantages in enhancing vocabulary acquisition
and retention, beyond traditional learning methods[33].
Most studies utilizing artificial intelligence in language
education predominantly focus on outlining the AI tools em-
ployed for language teaching, yet they often overlook the
exploration of ethical and responsible utilization methods of
these AI tools. To get insight into how to effectively tackle
these concerns and establish a framework that is both respon-
sible and sustainable, we may explore the use of AI as an
independent entity for language instruction and evaluate the
possible ethical hazards it may entail.
Considering the widespread use of AI in language ed-
ucation, including its various applications and benefits, it
becomes crucial to emphasize the responsible and ethical use
of AI in this field. The researchers continuously investigate
the literature to identify the types and tendencies of recent
studies, ensuring the responsible and ethical use of AI in
language education. This will aid in comprehending current
practices and guiding future research in the field, focusing
on two key research questions:
1.
What are the emerging patterns and practices in REALE?
2.
What research methodologies have been utilized in stud-
ies examining REALE?
Therefore, this study comprehensively examines
REALE from various perspectives, including the distribu-
tion of research themes and the methodological features of
the REALE investigations. It also provides comprehensive
summaries and annotated references on the subject. By metic-
ulously examining the range of study subjects, the objective
and the methodological features, it provides a more compre-
hensive understanding of REALE.
3. Methodology
The Preferred Reporting Items for Systematic Reviews
and Meta-Analyses (PRISMA) framework was used to guide
this study. It has four main steps: identification, screening,
eligibility and inclusion (see Figure 1). The extensive scope
and adaptability of PRISMA have rendered it a preferred
instrument among researchers. This research aims to deter-
mine the purpose of this study and outline the methodology
for conducting a systematic review.
Figure 1. The PRISMA systematic review [34].
3.1. Identification
The PRISMA guidelines offer a structured approach
for the initial identification phase of any systematic review.
The researchers selected the databases Web of Science and
Scopus as the primary sources of data for this study. The
key search phrases were meticulously crafted to ensure they
accurately reflected the concepts under investigation, incor-
porating a range of terms relevant to REALE. The specific
search queries used in this study are detailed in Table 1.
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Table 1. Search string used in this study.
Database Search String
Web of Science (WoS)
TS = ((“artificial intelligence” OR “AI”) AND (“responsible” OR “ethical” OR “ethics”) AND (“adoption” OR
“implementation” OR “use”) AND (“language education” OR “language learning” OR “language teaching” OR
“language pedagogy” OR “language instruction”))
Scopus
TITLE-ABS-KEY ((“artificial intelligence” OR “AI”) AND (“responsible” OR “ethical” OR “ethics”)AND
(“adoption” OR “implementation” OR “use”) AND (“language education” OR “language learning” OR
“language teaching” OR “language pedagogy” OR “language instruction”))
3.2. Screening
Once 65 articles are identified, they undergo screen-
ing, starting with the removal of duplicates that appear in
more than one database. The preliminary evaluation elimi-
nated 3 redundant articles, leaving a total of 62 articles. The
researchers examined the titles, abstracts and keywords of
these 62 articles in order to ascertain their pertinence to the
topic of “Responsible and Ethical Artificial Intelligence in
Language Education”. Through the screening process, 20
publications were excluded as they were considered irrele-
vant to the study’s objectives. Table 2 presents the outcomes
of the inclusion/exclusion screening process that was carried
out on the remaining 42 articles.
Table 2. Inclusion and exclusion criteria.
Inclusion Criteria Exclusion Criteria
Studies conducted between 2020 and 2024 Studies conducted before 2020
Articles from open access journals Articles that are not published in open access journals
Articles from journals Conference proceedings, review articles and books
The text was written in English Text not written in English
Related to REALE Not related to REALE
The inclusion of publications in this systematic review
was based on the assessment of 9 articles. These articles were
first examined to determine if they met specific criteria for
inclusion or exclusion. The researchers conducted a review,
excluding book chapters and conference proceedings due to
their relative lack of comprehensiveness[35].
3.3. Included
This literature study focused on REALE and were
chosen from Scopus and WoS, as shown in Table 3. The
databases were chosen based on the exceptional calibre of the
instructional content they contain. This study dedicated each
investigation to examining a specific facet of the REALE,
with the majority occurring in higher education environ-
ments.
3.4. Data Analysis Procedure
The selected publications were imported into Mende-
ley, a citation management tool and subsequently arranged,
annotated and categorised according to their relevance to the
research enquiries. This study employed thematic analyses
to address the subsequent research problems.
Table 3. Summary of the selected studies.
Study Database
Yang et al. (2024) [36] Scopus, WoS
Ružić & Balaban (2024)[37] Scopus, WoS
Ross & Baines (2024)[38] Scopus, WoS
Cong-Lem et al. (2024) [39] Scopus
Hieu & Thao (2024)[40] Scopus
Ivanytska, et al. (2024) [41] WoS
Avsheniuk et al. (2024) [42] WoS
Noroozi et al. (2024) [43] WoS
Joseph (2023)[44] WoS
4. Findings and Discussion
4.1.
The Emerging Patterns and Practices in
REALE
The escalating quantity of publications published be-
tween 2020–2024 provides evidence of the rising interest
in REALE over the past 5 years, particularly in the after-
math of the COVID-19 pandemic. These selected studies
were analyzed using thematic codes: the objective of the
study, methodologies applied, sample, country and the key
outcomes as shown in Table 4.
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Table 4. Patterns and Practices of REALE.
Study Objective Methodology Sample Country Outcome
Yang et al.
(2024)[36]
To examine the ethical
considerations and
emotional reactions linked
to the utilisation of AI
chatbots in educational
environments
Mixed Method
Articles from
Web of Science
and SpringerLink
China
The transformative potential of
ChatGPT in education and the
need for careful consideration of
ethical implications and the
emotional impact on students
Ružić & Balaban
(2024)[37]
To examine the utilisation
of AI in primary and
secondary education,
focussing on the
theoretical underpinnings,
areas of application and
ethical concerns
Qualitative
Articles from
Web of Science
and Scopus
Croatia
A need for extensive studies to
integrate AI into education and
establish clear guidelines to
harness the ethical considerations
and data privacy concerns
Ross & Baines
(2024)[38]
To discuss the benefits,
drawbacks and ethical
considerations of
generative AI
Mixed method
Staff and students
from the
Department of
Classics,
University of
Reading
United
Kingdom
By imparting the ethical issues of
generative AI to staff and
students, they can make
well-informed assessments
regarding the utilisation of AI in
their work, devoid of
unwarranted trust or undue
apprehension
Cong-Lem et al.
(2024)[39]
To explore EFL teachers’
views on what they
consider academic
dishonesty involving AI
and examines the
strategies they use or plan
to use in response
Mixed Method
31 EFL teachers
from various
institutions
Vietnam
The teachers predominantly
viewed plagiarism, absence of
innovative concepts and use of
AI-generated content without
appropriate acknowledgement as
manifestations of academic
dishonesty
Hieu & Thao
(2024)[40]
To examine the difficulties
and potential advantages
of incorporating ChatGPT
into language instruction
methods
Qualitative
9 EFL teachers
from 2
educational
institutions
Vietnam
The study identified the
challenges and opportunities in
integrating ChatGPT into
language teaching including
cultural and contextual
misalignments, language
accuracy issues and ethical
considerations
Ivanytska, et al.
(2024)[41]
To emphasize the
significance of teachers’
proficiency in negotiating
the ethical ramifications of
integrating AI into
education
Quantitative
86 EFL students
from various
Ukrainian
universities
Ukraine
A need for a method of
integrating AI into foreign
language instruction that
effectively combines the
advantages of teaching with the
necessary precautions to
maintain the standard and
authenticity of the educational
experience
Avsheniuk et al.
(2024)[42]
To examine the impact of
ChatGPT on critical
thinking skills and
proficiency in the English
language
Mixed Method
31 students and 3
language
instructors from
English
departments
Ukraine
Various perspectives on the
effectiveness of ChatGPT, its
influence on critical thinking, the
enhancement of English
language abilities and ethical
concerns
Noroozi et al.
(2024)[43]
To address ethical
concerns associated with
the utilisation of
Generative AI in the field
of education
Qualitative
17 articles from
SSCI-indexed
journals
Netherlands
A need for an ethical guideline,
to ensure responsible integration
in diverse educational contexts
Joseph (2023)[44]
To develop a framework
for integrating LLM-based
tools like ChatGPT into
language teaching
Qualitative
17 articles from
Google Scholar
and
ScienceDirect
India
To address ethical considerations
related to the use of Large
Language Model-based tools in
education
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Studies from Table 4 reveal a growing trend towards
the ethical and responsible use of AI in education, with a
particular focus on addressing the challenges and ethical
considerations associated with AI tools such as chatbots and
large language models. The COVID-19 pandemic has ac-
celerated interest in these technologies, as educators seek
innovative ways to engage students in remote and distributed
learning environments. Methodologically, there has been
a noticeable prevalence of mixed methods and qualitative
studies, with only one quantitative study present, allowing
researchers to explore both the quantitative outcomes and
the qualitative experiences of students and educators with AI
tools. This trend reflects a broader recognition of the need to
balance technological innovation with ethical responsibility
in education.
The practices identified in the literature highlight the
diverse applications of AI in educational settings. These
include using AI-driven chatbots to enhance student engage-
ment, applying large language models for personalized learn-
ing experiences and addressing academic dishonesty through
AI monitoring tools. These studies highlight the significance
of formulating unambiguous guidelines and frameworks to
ensure the ethical integration of AI into education while
protecting student data privacy. The field is increasingly
emphasizing not only the technological capabilities of AI,
but also its ethical implications and the need for educators
to receive adequate training to effectively navigate these
challenges.
4.2.
The Research Methodologies Utilized in
Studies Examining the REALE
The systematic review included 9 studies, all of which
employed three distinct study methodologies: mixed meth-
ods, qualitative, and quantitative. Overall, most studies on
REALE patterns and practices favoured a qualitative and
mixed-methods approaches, which is reflected in the equal
proportion of qualitative and mixed-methods studies in the
reviewed literature. Quantitative methods were the least
common, with only one study employing this approach.
Qualitative and mixed-methods approaches were the
most frequently employed in the reviewed studies. These ap-
proaches focused on gathering rich, in-depth insights into the
experiences and perspectives of educators and students using
AI in language education. Four studies used qualitative meth-
ods, such as case studies, to explore the nuances of ethical
AI implementation in classrooms, while four studies utilized
mixed methods to gain a comprehensive understanding by
integrating both quantitative and qualitative data. These
studies highlighted the challenges and ethical concerns as-
sociated with AI use, as well as the perceived impacts on
teaching and learning. Despite the potential for in-depth
understanding, qualitative research in REALE has faced crit-
icism due to issues like small sample sizes and challenges
in establishing reliability and validity
[45–47]
. However, these
studies provided essential contextual information that quan-
titative methods might overlook. Examining qualitative data
can be challenging, iterative, intricate, perplexing, and time-
intensive, despite its ability to offer substantiated, elaborate,
and extensive accounts of individuals’ experiences and rea-
soning regarding the relevant issues[48–50].
Only one study employed a quantitative approach,
which focused on statistical analysis to assess the impact
and effectiveness of AI tools in language education. This
study utilized experimental designs, coupled with surveys
and questionnaires to collect data. The use of these method-
ologies allowed the researcher to generate reliable, objective,
and statistically significant results, which are crucial for eval-
uating the ethical and responsible use of AI in educational
contexts. Agarwal et al. and Allen et al. have highlighted the
preference for quantitative methods due to their reliability
and objectivity
[51, 52]
. These methods offered clear metrics
and quantifiable data for evaluating the outcomes of AI im-
plementation in educational settings. The findings of this
review corroborate the claims put out by other previous re-
searches that quantitative methods are less common among
educational scholars[53–55].
Mixed-methods studies integrated questionnaires with
open-ended inquiries or semi-structured interviews to offer a
comprehensive perspective on the use and perception of AI
tools in language education. There are researchers argue that
mixed-method designs are particularly effective in education
research because they allow for triangulation of data, which
enhances the validity and reliability of the findings
[56–59]
.
The use of mixed methods in REALE studies, while equally
prevalent as qualitative methods, is commendable for its
ability to address complex research questions from various
perspectives. Researchers praise the mixed-method approach
for its ability to obtain triangulated data, thereby enhancing
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Forum for Linguistic Studies | Volume 06 | Issue 05 | November 2024
the validity and comprehensiveness of evidence supporting
the application of REALE approaches in language teaching
and learning[60, 61].
5. Conclusions
The present study conducted a systematic literature
analysis, examining 9 publications released from 2020 to
2024 to address the research issue regarding the current state
of research patterns and practices in REALE. The conclu-
sions of this review have consolidated the current knowledge
in REALE research, covering key aspects such as the ob-
jectives, methodologies, samples, countries of study and
outcomes. The investigation revealed that while REALE
research is still in its nascent stages, there is significant
growth and potential in this field. The findings highlight
the importance of continued exploration and development to
effectively implement REALE at all educational levels.
This study, like several others, has inherent limitations
that present opportunities for further investigation. The scope
of this review was limited to the journals included in the
analysis, primarily those listed in WoS and Scopus. The
increasing volume of articles in REALE made it challeng-
ing to conduct a comprehensive and exhaustive search. The
criteria formulated for selecting publications, though rigor-
ous, may have excluded relevant studies published in other
reputable sources. Additionally, the review focused predom-
inantly on studies related to English as a foreign Language
(EFL), which may not fully represent the diversity of REALE
research across different languages and educational contexts.
Given the limitations identified, future research should
broaden the scope of REALE studies to include additional
sources such as conference proceedings, project reports and
academic dissertations that involve languages other than En-
glish, such as Arabic, Mandarin, French, Korean or Japanese.
Expanding the timeframe and including a wider range of
research emphases could reveal more extensive patterns and
shifts in the evolution of REALE over time. Future stud-
ies should investigate whether people utilize REALE as an
independent modality or as part of established courses or
programs with robust pedagogical frameworks. These in-
vestigations would provide valuable insights into the most
effective ways to integrate REALE into diverse educational
settings.
Author Contributions
Conceptualization, N.Z. and N.A.S.; methodology,
N.Z.; software, M.S.S.; validation, N.M.N.; formal anal-
ysis, N.A.S.; investigation, M.N.J.; resources, W.A.A.W.D.
and M.T.A.G.; data curation, N.A.S.; writing—original draft
preparation, M.N.J.; writing—review and editing, N.M.N.;
visualization, W.A.A.W.D.; supervision, N.Z.; project ad-
ministration, M.T.A.G.; funding acquisition, N.Z. All au-
thors have read and agreed to the published version of the
manuscript. Authorship must be limited to those who have
contributed substantially to the work reported.
Funding
This research was funded by the Ministry of
Higher Education (MoHE) of Malaysia under the Fun-
damental Research Grant Scheme with reference number
(FRGS/1/2024/SSI09/USIM/02/5).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data that support the findings of this study are
openly available in the references section of this article and
can be accessed through the links in this section. All relevant
data and materials used in this study are included in these
sources and are accessible through the provided links. There
are no restrictions on access to these data.
Conflicts of Interest
The authors declare no conflict of interest.
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