9

Redefining Language Education

in the AI Era: Challenges,
Opportunities and Perspectives

Miguel Cuevas-Alonso
University of Vigo, Spain
miguel.cuevas@uvigo.gal
https://orcid.org/0000-0001-7656-2374

Pablo M. Tagarro
University of the Basque Country, Spain
pablobec93@hotmail.es
https://orcid.org/0009-0003-8220-7792

Abstract
Recent years have seen a substantial evolution in the nascent field of artificial
intelligence (AI), influencing a wide range of disciplines. The domain of lan-
guage teaching and learning is similarly undergoing a transformation driven
by this technological upheaval, that of Industry 4.0. However, the integration
of AI in this field is often undertaken without sufficient reflection, despite the
profound social and personal implications it entails, including ethical concerns
and data protection issues. The objective of this chapter is, essentially, three-
fold: 1) it contextualises language teaching within the burgeoning technologi-
cal milieu, underscoring the interplay between AI and language education;
2) it explores the challenges and opportunities in language teaching arising
from AI integration; and 3) it explores the potential of AI to enhance the effi-
ciency and effectiveness of language education, while also critically examining
the possible adverse effects that its application might bring about.

Keywords: artificial intelligence, second language acquisition, language edu-

cation, language learning.

https://doi.org/10.36006/09651-1-09 135
 9.1. Introductory Remarks
The emergence of artificial intelligence (henceforth AI) within
contemporary society has brought with it significant transforma-
tions, revolutionising the approach not only to mundane tasks
but also to those of a more specialised nature (see, e.g., Russell
et al., 2022, for an overview of the field). In this context, numer-
ous scholars acknowledge the advent of what is often referred to
as the Fourth Industrial Revolution (or Industry 4.0), a period
characterised by the integration of advanced technologies such as
AI into sectors like healthcare, finance, transportation, entertain-
ment and the media, human resources and education. Within
language education, AI is poised to assume diverse roles in all
these fields, serving as a tutor, a learning facilitator, and even an
advisor, according to Dakakni & Safa (2023). The potential of AI
to mimic human thought processes – such as learning, reasoning,
memory, planning and problem-solving – is significant. Com-
bined with its capabilities in voice and image recognition, natural
language processing (NLP), and multidimensional factor analysis
(Abdullah Sharadgah & Abdulatif Sa’di, 2022), AI is providing
substantial (and obvious) benefits in the field of language educa-
tion. Furthermore, the previous decade has been characterised by
unprecedented developments in deep learning technologies
(Surdeanu & Valenzuela-­Escárcega, 2024; see also Goodfellow
et al., 2016, for a more general review) departing from symbol-
ic approaches to NLP and, by extension, to AI (see Gómez-Pérez,
2023, pp. 57 and ff., for further information). In fact, language-
centric AI is “undergoing a paradigm shift with the rise of neural
language models that are trained on broad data at scale and are
adaptable to a wide range of monolingual and multilingual
downstream tasks” (Agerri et al., 2023, p. 16).
Nonetheless, for the purpose of proposing the responsible
use of these technological advancements, it is crucial to acknowl-
edge that a number of ethical dilemmas are subject to debate.
These challenges extend beyond the general implications associ-
ated with the use of AI to encompass issues related to language,
linguistic policy and the phenomenon of linguistic cybercoloni-
alism. Moreover, the rapid pace of technological advances here
scarcely affords adequate time for the execution of comprehen-
sive studies aimed at assessing the benefits and potential risk fac-

136 The Education Revolution through Artificial Intelligence

tors involved. Consequently, this acceleration brings with it the
risk of insufficient understanding of the manner in which these
technologies alter various facets of life and human behaviour
(Jenks, 2023).

9.2. The Interplay between Language Teaching

and AI
The domain of language teaching is one sphere of activity in
which AI exerts a considerable influence. As shown in subsequent
sections of this chapter, AI facilitates the adoption of innovative
methodologies that significantly bolster the learning process,
such as gamification (i.e., the application of elements of game de-
sign in educational contexts to increase motivation and learning
outcomes). Such an enhancement is attributable to four factors in
particular (Akgun & Greenhow, 2022; Caldarini et al., 2022; Chen
et al., 2020; Dakakni & Safa, 2023; Roll & Wylie, 2016; Wei, 2023;
Zhang et al., 2020): 1) the scope for personalised learning,1 fun-
damentally through the adaptation of content and pace of learn-
ing to accommodate individual requirements, this facilitated by
the capacity of AI to identify specific learning difficulties; 2) the
adoption of hybrid instructional models, which integrate tradi-
tional face-to-face teaching with technologically mediated educa-
tion; 3) the provision of augmented support for learners engaged
in the development of collaborative projects; and 4) real-time in-
teraction with intelligent systems that can simulate real-life lin-
guistic interaction in natural, realistic contexts, although with dif-
ficulties in replicating cultural and contextual nuances (e.g., idi-
oms, colloquialisms, etc.) characteristic of natural languages (Re-
bolledo-Font-de-la-Vall & González-Araya, 2023).

1. Although findings are not consistent, some studies have shown that AI can sus-
tain the quality of student feedback and intrinsic motivation, and can enhance the effi-
cacy of self-monitoring in preserving student performance, thus promoting a sense of
empowerment in their self-regulated learning practices. Also reported is the possibility
that groups trained using AI make additional effort in their peer reviews, resulting in
more extended comments; such lengthier comments have been linked to improved
learning and self-regulation, and to a reduction in student anxiety (Darvishi et al., 2024;
Lai et al., 2023; Wei, 2023). It is crucial, then, to acknowledge the significance of affective
states and motivation in the success of language learning performance (Dewaele, 2022).

9. Redefining Language Education in the AI Era 137

 This is pertinent to the framework of a constructivist ap-
proach in the realm of language pedagogy, underpinned as it is
by sociocultural theory (Lantolf & Pavlenko, 1995). As argued
by Kannan & Munday (2018, 14), “language learning is funda-
mentally a socio-cultural experience”. Concurrently, Blake
(2017) observes that collaboration between two or more learn-
ers is likely to yield more sophisticated and precise expressions
in a foreign language. Furthermore, applying Situated Learning
Theory to language acquisition underscores the crucial role of a
community, where experienced speakers facilitate the learning
process for newcomers. This approach emphasises “the relation-
ship between learning and the social situation in which it oc-
curs” (Lave & Wenger, 2009, p. 14), making clear the impor-
tance of contextual and social dimensions in learning processes.
At the same time, and as noted by Anderson et al. (1996), we
should acknowledge that the situated nature of learning is not a
universal requisite for all learning experiences, although it is
greatly beneficial in the case of language learning. In this con-
text, it appears that AI could, to a certain extent, fulfill the role
of an interlocutor in this form of learning, especially if the pro-
cess becomes wholly immersive. This, in turn, would have pro-
found effects on the conceptualisation of interaction within lan-
guage teaching and learning. Moreover, it potentially entails
shifts in linguistic behaviour on a global scale (Jenks, 2023).
The ability of AI to simulate real-life situations through mul-
timodal teaching learning is of particular import. To date,
achievements have remained elusive, despite efforts to this end
within communicative (or interactive-based) and sociocultural
methodologies. Yet there is the potential for the great success of
AI here, moving beyond the era of Computer Assisted Language
Learning (CALL) that has been dominant over the past 30 years.
It also has the potential to influence and enhance more recent
advancements like Mobile Assisted Language Learning (MALL),
evolving towards Intelligent CALL and Networked Learning
(NL) (Kannan & Munday, 2018). These developments represent
a shift towards “learning in which ICT [Information and Com-
munication Technology] is used to promote connections be-
tween one learner and other learners, between learners and tu-
tors, and between a learning community and its learning re-
sources” (Jones, 2015, p. 5).

138 The Education Revolution through Artificial Intelligence

 In a similar vein, the integration of virtual reality (VR) along-
side AI in language learning should be emphasised. Some stud-
ies highlight the benefits that VR can offer in language learning
in a variety of ways: the creation of real-life contexts, a reduction
in anxiety, etc. (Ma, 2021; Tai & Chen, 2021; Melchor-Couto &
Herrera, 2022; Gruber et al., 2023; Kaplan-Rakowski & Gruber,
2023; Ironsi, 2023). In this respect, multimodality (Bateman,
2021; Kress & Van Leeuwen, 2001) currently constitutes the
framework within which any language learning should take
place, alongside the sociocultural approach (Dressman, 2019;
Guo, 2023). Whereas technology applied to language teaching
has thus far incorporated this multimodal dimension gradually
and to a certain extent (Herrero, 2023), AI could facilitate sub-
stantial improvements here, including the ability to conceptual-
ise communication within the realms of language education
and acquisition as transcending mere aggregations of utterances.
AI possesses the capacity to analyse and generate messages that
incorporate a multiplicity of communicative modalities, engen-
dering a variety of meanings through the use of heterogeneous
semiotic resources (including, of course, those of a social na-
ture). This is exemplified by the recent development of Google’s
multimodal AI model Gemini (Durante et al., 2024), particu-
larly as further progress is made in the transition from multi-
modal understanding models to multimodal generation mod-
els, and with the application of compositional AI. The latter is
understood as the use of AI modules with diverse functions that
combine to address complex problems, from the amalgamation
of which new capabilities emerge, ones which are unattainable
for a single module (Du & Kaelbling, 2024; Martie et al., 2023;
see also Wei et al., 2022, for emergent abilities of large language
models). In addition, the capacity of AI to furnish instantaneous
feedback on the progression of linguistic competencies must be
underscored, an issue that is of key importance in language
learning, given that feedback can propel the learning process
(see Chen et al., 2024).
It is thus likely that the use of AI in the years ahead will ena-
ble not only the establishment of personalised language-learn-
ing programmes, but also the reduction of the linguistic gap be-
tween what is taught in classrooms and the reality of languages
in use. This approach brings students closer to a sociolinguisti-

9. Redefining Language Education in the AI Era 139

 cally immersive reality, by allowing for the consideration and
adjustment of content based on the linguistic distance between
the macro and micro levels of language use, as well as the cul-
tural, linguistic, and social diversities of linguistic practices.
Within this discourse, several scholarly contributions recom-
mend eclectic methodologies that integrate conventional para-
digms with innovative, technology-based methodologies, in-
cluding AI. Consequently, the synthesis of pedagogical strategies
that accentuate the communicative and contextual dimensions
of language usage and acquisition – the communicative ap-
proach, whole language approach, etc. – seems to facilitate an
appropriate means of embedding AI into language education.
This integration does not invariably sideline the teacher, and it
has been argued convincingly that both blended and situated
learning methodologies tend to produce favourable outcomes
(Valledor et al., 2023).
As will be discussed below, tools such as intelligent chatbots
facilitate language practice in real-world contexts, while adaptive
systems tailor content to the user’s level, thereby optimising the
learning process. AI also contributes to the analysis of linguistic
outputs, enabling precise feedback on pronunciation and gram-
mar, and provides learning recommendations based on the us-
er’s progress and preferences. This significantly enhances the ed-
ucational experience and could even alleviate the anxiety associ-
ated with foreign language production tasks (Abdullah
Sharadgah & Abdulatif Sa’di, 2022).

9.3. AI-Based Technology for Language Learning

AI technologies as applied to language learning scenarios en-
compass a multi-faceted range of integrations, as described in
Pokrivcakova (2019; see also Abdullah Sharadgah & Abdulatif
Sa’di, 2022, for the use of AI in English teaching and learning).
These range from intelligent tutoring systems (ITS) and chatbots
(also conversational agents, virtual assistants or pedagogical
agents) (Hwang & Chang, 2023; Zhai & Wibowo, 2022) to
speech recognition prototypes. Son et al. (2023), for their part,
review AI’s role in foreign language learning, highlighting a fu-
ture where AI-supported tools become integral to language edu-

140 The Education Revolution through Artificial Intelligence

cation, covering seven areas of application: NLP, data-driven
learning, automated writing evaluation (AWE), computerised
dynamic assessment, intelligent tutoring systems, automatic
speech recognition and chatbots.
Gkountara & Prasad (2022), for instance, present an overview
of variegated AI implementations within the domain of (for-
eign) language learning. They outline how AI-based technology
can enhance diverse aspects of learning here, such as automated
speech recognition (ASR) for pronunciation and oral proficiency
training (see Agarwal & Chakraborty, 2019; Liu et al., 2022); the
development of tailored syllabuses that adapt to learners’ pro-
gress; and the use of virtual and augmented reality to gamify
learning and promote collaborative learning (see Hung et al.,
2018). They also highlight the optimisation of learning applica-
tions to accommodate different learning styles (e.g., Duolingo);
the evaluation of text readability (e.g., ReaderBench; see Dascalu
et al., 2013); the use of AI-powered translation tools (e.g., Goog-
le Glass Enterprise Edition or Google Pixel Buds); and AWE to
improve writing skills, support learner autonomy, and reduce
teachers’ workload by providing immediate, detailed feedback
(see Zhang, 2021, for a review of AWE systems and the impor-
tance of navigating challenges such as the effective comprehen-
sion of AWE feedback among both educators and learners). Ad-
ditionally, Gkountara & Prasad (2022) showcase robot-assisted
language learning (RALL), as a subfield of human-robot interac-
tion (HRI), for interactive engagement, potentially offering
unique advantages over computer-assisted language learning
(CALL) (albeit more research is needed to establish robust de-
sign and implementation guidelines) (see Randall, 2020), in-
cluding: the use of AI to mitigate language learning anxiety; per-
sonalised feedback through formative assessment with AI and
machine learning techniques; and the facilitation of computer
mediated communication (CMC) and storytelling (e.g., Mentira,
Holden & Sykes, 2012).

9.4. Ethical Considerations

As we know, a substantial number of ethical considerations en-
velop AI, particularly within its educational applications (Bod-

9. Redefining Language Education in the AI Era 141

 dington, 2023; Nguyen et al., 2023; Satpute, 2023; UNESCO,
2019, 2021). This chapter focuses exclusively on those aspects
which, in relation to language teaching, have specific, significant
importance.
Firstly, during the development of individualised learning tra-
jectories, AI constructs models of both learners and educators, in-
corporating data on their emotional, social, motivational and lin-
guistic states, alongside their preferences within these domains. As
indicated in numerous studies, this raises the potential for infring-
ing or compromising privacy (Akgun & Greenhow, 2022) and,
among other concerns, it also intersects with issues pertaining to
linguistic rights, such as the choice of language and its dialects, for
instance.
On the other hand, the challenge of distinguishing between
oral/written texts produced by humans or by AI (Farhi et al.,
2023; Renzella et al., 2022; Susnjak, 2022; Tlili et al., 2023) rep-
resents a recurrent issue in contemporary language teaching in
formal contexts. Students often use this technology not to learn
languages, but simply to help them pass their courses, possibly
because AI is not integrated into classrooms as a supportive tool,
but rather is seen as a form of plagiarism (for the time being,
largely an undetectable one). Therefore, it is crucial to encourage
both educators and students to reflect on the ethical dimensions
of using these tools in academic settings, and on the importance
of individual linguistic creativity in language learning, without
demonising the use of AI-based technologies.
A critical issue to consider in the relationship between AI and
language teaching is the status of minority languages in this
Fourth Industrial Revolution. A “Digital Language Extinction”
would affect not only minority languages but also those which,
although of majority use in a specific area, are minority ones in
other countries (see Kornai, 2013). This situation has been espe-
cially apparent in the field of automatic translation, as noted by
Jenks (2023). However, there is also the risk that, within the
context of language education, the increased prominence of ma-
jority languages, particularly those serving as a lingua franca (i.e.,
English in the contemporary global context, see Crystal, 2003)
and perceived (erroneously) as inherently more valuable, might
lead to further marginalisation, if not the effective extinction, of
languages with fewer speakers.

142 The Education Revolution through Artificial Intelligence

 This is not a trivial matter, and has led European institutions
(Council of Europe Secretariat of the European Charter for Re-
gional or Minority Languages, 2022; European Parliament,
2018) to issue a series of reports aimed at mitigating the precari-
ous situation of numerous languages at risk of digital extinction,
in order to fulfill the obligations of the European Charter for Re-
gional or Minority Languages.2 UNESCO has also voiced its con-
cern through various resolutions that seek to promote multilin-
gualism and protect minority languages in the digital domain. In
this context, AI can be seen as offering dichotomous potentiali-
ties: on the one hand, it offers positive ones, in that it can serve
as an invaluable means of collaboration in the development of
educational applications targeting the preservation and expan-
sion of those languages in danger, provided that it is aptly de-
signed and trained for such endeavours. On the other hand,
there exists a plausible risk that the capabilities of generative AI
for these languages might be severely limited due to their under-
representation in available datasets.
Not all languages enjoy the same levels of technological, so-
cial, political and economic supports to ensure their continu-
ance in the AI era, leading to an additional degree of digital col-
onisation by predominant languages. In their examination of
thirty European nations, Rehm & Way (2023, p. 38) point out
that “with the exceptions of English, German, French and Span-
ish, all languages we investigated exist in socio-political and
economic ecosystems that do not incentivize, encourage or fos-
ter the development of technologies for these languages. While
all 30 European countries we surveyed have put in place nation-
al AI strategies, almost all of these national strategies seem to
have either ignored or left out the topic of languages and lan-
guage-centric AI”.
This consideration is linked to the observation that algo-
rithms are inherently non-neutral, reflecting the values and bias-
es of their creators (Akgun & Greenhow, 2022; Alegria & Yeh,
2023; Karan & Angadi, 2023). Consequently, algorithms may,
one way or another, incorporate ideological profiles and biases
towards languages, fostering inequality, social stratification and

2. See also the “Report on the state of Language Technology in 2030” (Way et al.,
2022) from European Language Equality (2022).

9. Redefining Language Education in the AI Era 143

 discrimination based on linguistic variables, as well as conform-
ing to specific linguistic policies rooted in social, economic, or
strategic interests.3
Therefore, it is necessary to incorporate a framework of social
justice that also extends to languages and their pedagogy. Such a
framework must address and ameliorate inequalities towards en-
suring equitable access to technological advances, irrespective of
an individual’s socioeconomic status. This approach seeks to
prevent the exacerbation of the digital divide and the subsequent
marginalisation of particular communities, regardless of wheth-
er these concerns arise from the status of minority languages or
from socioeconomic and political circumstances.

9.5. Conclusion
In the current chapter, we have described both the benefits and
the ethical considerations associated with the deployment of AI
in the domain of language learning. Clearly, AI has the potential
to facilitate the development of tailored learning curricula that
not only align with the aspirations of learners but also meticu-
lously track their progression across all linguistic dimensions,
ranging from phonetic to pragmatic aspects, and spanning pro-
ductive, receptive, mediating, and interactive communicative
competences, as well as strategic skills. It is crucial to underscore
the great capacity of generative AI in developing multimodal en-
vironments which, through the integration of VR, will situate
learning within a thoroughly immersive experience. However,
given the apparent variations in effectiveness across disciplines,
a thorough exploration of this issue within the context of lan-
guage teaching and learning has become essential (Pumptow &
Brahm, 2023). The broad benefits of AI may entail certain draw-
backs, particularly if there is a lack of awareness regarding the
potential consequences that its implementation might have on
the use and learning of minority languages. In this context, the
need for social justice becomes evident, demanding the elimina-

3. Particularly noteworthy here is the VirtuSign project in terms of its innovative

integration of AI, facilitating a gamified environment that enables interactive learning
and practice of the American Sign Language (ASL) alphabet (Tukpah et al., 2023).

144 The Education Revolution through Artificial Intelligence

tion of biases in AI. Such biases can arise from the limited data
available for minority languages, as well as from the disparities
in access to technology due to socioeconomic factors.

References
Abdullah Sharadgah, T., & Abdulatif Sa’di, R. (2022). A systematic review
of research on the use of Artificial Intelligence in English language
teaching and learning (2015-2021): What are the current effects? Jour-
nal of Information Technology Education: Research, 21, 337-377.
Agarwal, C., & Chakraborty, P. (2019). A review of tools and tech-
niques for computer aided pronunciation training (CAPT) in Eng-
lish. Education and Information Technologies, 24, 3731-3743.
Agerri, R., Agirre, E., Aldabe, I., Aranberri, N., Arriola, J. M., Atutxa, A.,
Azkune, G., Campos, J. A., Casillas, A., Estarrona, A., Farwell, A.,
Goenaga, I., Goikoetxea, J., Gojenola, K., Hernáez, I., Iruskieta, M.,
Labaka, G., Lopez de Lacalle, O., Navas, E., Oronoz, M., Otegi, A.,
Pérez, A., Perez de Viñaspre, O., Rigau, G., Salaberria, A., Sanchez,
J., Saratxaga I., & Soroa, A. (2023). State-of-the-art in language tech-
nology and language-centric Artificial Intelligence. In G. Rehm, & A.
Way (Eds.). European Language Equality: A Strategic Agenda for Digital
Language Equality (pp. 13-38). Springer..
Akgun, S., & Greenhow, C. (2022). Artificial Intelligence in education: Ad-
dressing ethical challenges in K-12 settings. AI and Ethics, 2, 431-440.
Alegria, S., & Yeh, C. (2023). Machine learning and the reproduction of
inequality. Contexts, 22(4), 34-39.
Anderson, J. R., Reder, L. M., & Simon, H. A. (1996). Situated learning
and education. Educational Researcher, 25(4), 5-11.
Bateman, J. A. (2021). What are digital media? Discourse, Context & Me-
dia, 41, 100502.
Blake, R. J. (2017). Technologies for teaching and learning L2 speaking.
In C. A. Chapelle, & S. Sauro (Eds.). The Handbook of Technology and
Second Language Teaching and Learning (pp. 107-117). Wiley.
Boddington, P. (2023). AI Ethics: A Textbook. Springer.
Caldarini, G., Jaf, S., & McGarry, K. (2022). A literature survey of recent
advances in chatbots. Information, 13(1), 41.
Chen, B., Bao, L., Zhang, R., Zhang, J., Liu, F., Wang, S., & Li, M. (2024).
A multi-strategy computer assisted EFL writing learning system with
deep learning incorporated and its effects on learning: A writing

9. Redefining Language Education in the AI Era 145

 feedback perspective. Journal of Educational Computing Research,
61(8), 60-102.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in education:
A review. IEEE Access, 8, 75264-75278.
Council of Europe Secretariat of the European Charter for Regional or
Minority Languages. (2022). Facilitating the Implementation of the
European CHARTER for Regional or Minority Languages through Artifi-
cial Intelligence. Council of Europe.
Crystal, D. (2003). English as a Global Language (2nd ed.). Cambridge UP.
Dakakni, D., & Safa, N. (2023). Artificial Intelligence in the L2 class-
room: Implications and challenges on ethics and equity in higher
education. A 21st century Pandora’s box. Computers and Education:
Artificial Intelligence, 5, 100179.
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024).
Impact of AI assistance on student agency. Computers & Education,
210, 104967.
Dascalu, M., Dessus, P., Trausan-Matu, Ş., Bianco, M., & Nardy, A.
(2013). ReaderBench, an environment for analyzing text complexity
and reading strategies. In H. C. Lane, K. Yacef, J. Mostow & P. Pavlik
(Eds.). Artificial Intelligence in Education. AIED 2013. Lecture Notes in
Computer Science (pp. 379-388). Springer.
Dewaele, J. M. (2022). Current trends in research in language education
and applied linguistics. The European Educational Researcher, 5(1), 1-4.
Dressman, M. (2019). Multimodality and language learning. In M.
Dressman, & R. W. Sadler (Eds.). The Handbook of Informal Language
Learning (pp. 39-55). Wiley.
Du, Y., & Kaelbling, L. (2024). Compositional generative modeling: A
single model is not all you need. arXiv:2402.01103.
Durante, Z., Huang, Q., Wake, N., Gong, R., Park, J. S., Sarkar, B., Taori, R.,
Noda, Y., Terzopoulos, D., Choi, Y., Ikeuchi, K., Vo, H., Fei-Fei, L.,
& Gao, J. (2024). Agent AI: Surveying the horizons of multimodal
interaction. arXiv:2401.03568.
European Parliament. (2018). Language Equality in the Digital Age. Eu-
ropean Parliament.
Farhi, F., Jeljeli, R., Aburezeq, I., Dweikat, F. F., Al-shami, S. A., &
Slamene, R. (2023). Analyzing the students’ views, concerns, and
perceived ethics about chat GPT usage. Computers and Education: Ar-
tificial Intelligence, 5, 100180.
Gkountara, D. N., & Prasad, R. (2022). A review of Artificial Intelli-
gence in foreign language learning. In 2022 25th International Sym-

146 The Education Revolution through Artificial Intelligence

 posium on Wireless Personal Multimedia Communication (WPMC)
(134-139), IEEE.
Gómez-Pérez, A. (2023). Inteligencia artificial y lengua española. RAE.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. The
MIT Press.
Gruber, A., Canto, S., & Jauregi-Ondarra, K. (2023). Exploring the use
of social virtual reality for virtual exchange. ReCALL, 35(3), 258-
273.
Guo, X. (2023). Multimodality in language education: Implications of
a multimodal affective perspective in foreign language teaching.
Frontiers in Psychology, 14, 1283625.
Herrero, C. (2023). Integrating screen media into the language curricu-
lum. In C. Herrero, & M. F. Suárez (Eds.). Teaching Languages with
Screen Media (pp. 47-68). Bloomsbury Academic.
Hung, H. T., Yang, J. C., Hwang, G. J., Chu, H. C., & Wang, C. C.
(2018). A scoping review of research on digital game-based lan-
guage learning. Computers & Education, 126, 89-104.
Hwang, G. J., & Chang, C. Y. (2023). A review of opportunities and
challenges of chatbots in education. Interactive Learning Environ-
ments, 31(7), 4099-4112.
Ironsi, C. S. (2023). Investigating the use of virtual reality to improve
speaking skills: Insights from students and teachers. Smart Learning
Environments, 10(1), 53.
Jenks, C. J. (2023). New Frontiers in Language and Technology. Cam-
bridge UP.
Jones, C. (2015). Networked Learning: An Educational Paradigm for the
Age of Digital Networks. Springer.
Kannan, J., & Munday, P. (2018). New trends in second language learn-
ing and teaching through the lens of ICT, networked learning, and
Artificial Intelligence. Círculo de Lingüística Aplicada a la Comuni-
cación, 76, 13-30.
Kaplan-Rakowski, R., & Gruber, A. (2023). The impact of high-immer-
sion virtual reality on foreign language anxiety. Smart Learning Envi-
ronments, 10(1), 46.
Karan, B., & Angadi, G. R. (2023). Potential risks of Artificial Intelli-
gence integration into school education: A systematic review. Bulle-
tin of Science, Technology & Society, 43(3-4), 67-85.
Kornai, A. (2013). Digital language death. PLOS ONE, 8(10), e77056.
Kress, G. R., & Van Leeuwen, T. (2001). Multimodal Discourse: The Modes
and Media of Contemporary Communication. Edward Arnold.

9. Redefining Language Education in the AI Era 147

 Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of
intrinsic motivation in ChatGPT adoption to support active learn-
ing: An extension of the technology acceptance model. Computers
and Education: Artificial Intelligence, 5, 100178.
Lantolf, J. P., & Pavlenko, A. (1995). Sociocultural theory and second lan-
guage acquisition. Annual Review of Applied Linguistics, 15, 108-124.
Liu, J., Liu, X., & Yang, C. (2022). A study of college students’ percep-
tions of utilizing automatic speech recognition technology to assist
English oral proficiency. Frontiers in Psychology, 13, 1049139.
Ma, L. (2021). An immersive context teaching method for college Eng-
lish based on Artificial Intelligence and machine learning in virtual
reality technology. Mobile Information Systems, 2021, 1-7.
Martie, L., Rosenberg, J., Demers, V., Zhang, G., Bhardwaj, O., Hen-
ning, J., Prasad, A., Stallone, M., Lee, J. Y., Yip, L., Adesina, D., Pai-
kari, E., Resendiz, O., Shaw, S., & Cox, D. (2023). Rapid develop-
ment of compositional AI. arXiv:2302.05941.
Melchor-Couto, S., & Herrera, B. (2022). Immersive virtual reality: Ex-
ploring possibilities for virtual exchange. In A. Potolia, & M. Derivry-­
Plard (Eds.). Virtual Exchange for Intercultural Language Learning and
Teaching. Fostering communication for the digital age (pp. 92-114).
Routledge.
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023).
Ethical principles for Artificial Intelligence in education. Education
and Information Technologies, 28, 4221-4241.
Pokrivcakova, S. (2019). Preparing teachers for the application of AI-
powered technologies in foreign language education. Journal of Lan-
guage and Cultural Education, 7(3), 135-153.
Pumptow, M., & Brahm, T. (2023). Higher education students differ in
their technology use. Computers and Education Open, 5, 100149.
Randall, N. (2019). A survey of robot-assisted language learning
(RALL). ACM Transactions on Human-Robot Interaction, 9(1), 1-36.
Rebolledo-Font-de-la-Vall, R., & González-Araya, F. (2023). Exploring
the benefits and challenges of AI-language learning tools. Interna-
tional Journal of Social Sciences and Humanities Invention, 10(01),
7569-7576.
Rehm, G., & Way, A. (2023). Strategic research, innovation and imple-
mentation agenda for digital language equality in Europe by 2030.
In G. Rehm, & A. Way (Eds.). European Language Equality : A Strategic
Agenda for Digital Language Equality (pp. 387-412). Springer.
Renzella, J., Cain, A., & Schneider, J. G. (2022). Verifying student iden-

148 The Education Revolution through Artificial Intelligence

 tity in oral assessments with deep speaker. Computers and Education:
Artificial Intelligence, 3, 100044.
Roll, I., & Wylie, R. (2016). Evolution and revolution in Artificial Intel-
ligence in education. International Journal of Artificial Intelligence in
Education, 26, 582-599.
Russell, S. J., & Norvig, P. et al. (2020). Artificial Intelligence: A Modern
Approach (4th ed.). Pearson.
Satpute, R. S. (2023). Transforming the language teaching experience
in the age of AI: Ethical, social, and cultural considerations in im-
plementing AI in language education. In G. Kartal (Ed.). Transform-
ing the Language Teaching Experience in the Age of AI (pp. 115-124).
IGI Global.
Son, J. B., Ružić, N. K., & Philpott, A. (2023). Artificial Intelligence
technologies and applications for language learning and teaching.
Journal of China Computer-Assisted Language Learning.
Surdeanu, M., & Valenzuela-Escárcega, M. A. (2024). Deep Learning for
Natural Language Processing: A Gentle Introduction. Cambridge UP.
Susnjak, T. (2022). ChatGPT: The End of Online Exam Integrity? arX-
iv:2212.09292
Tai, T. Y., & Chen, H. H. J. (2021). The impact of immersive virtual re-
ality on EFL learners’ listening comprehension. Journal of Education-
al Computing Research, 59(7), 1272-1293.
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T.,
Huang, R., & Agyemang, B. (2023). What if the devil is my guardian
angel: ChatGPT as a case study of using chatbots in education. Smart
Learning Environments, 10, 1-24.
Tukpah, J., Soevik, N., Bansal, S., Singh, T., & Yildirim, C. (2023). Virtu-
Sign: An AI-powered, gamified virtual reality application for the
American Sign Language alphabet. In F. Michahelles, P. Knierim, &
J. Häkkilä (Eds.). MUM ‘23: Proceedings of the 22nd International
Conference on Mobile and Ubiquitous Multimedia (pp. 538-540). ACM.
UNESCO (2019). Beijing Consensus on Artificial Intelligence and Educa-
tion. Outcome Document of the International Conference on Artificial
Intelligence and Education, planning education in the AI era: Lead the
leap. Beijing, United Nations Educational, Scientific and Cultural
Organization.
UNESCO (2021). Recommendation on the Ethics of Artificial Intelligence.
United Nations Educational.
Valledor, A., Olmedo, A., Hellín, C. J., Tayebi, A., Otón-Tortosa, S., &
Gómez, J. (2023). The eclectic approach in English language teach-

9. Redefining Language Education in the AI Era 149

 ing applications: A qualitative synthesis of the literature. Sustainabil-
ity, 15(15), 11978.
Way, A., Rehm, G., Dunne, J., Giagkou, M., Gomez-Perez, J. M., Hajič,
J., Hegele, S., Kaltenböck, M., Lynn, T., Marheinecke, K., Resende,
N., Skadiņa, I., Skowron, M., Vojtěchová, T., & Grützner-Zahn, A.
(2022). Report of the state of Language Technology in 2030. Euro-
pean Language Equality, D2.18, 1-50.
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yoga-
tama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E. H., Hashimoto,
T., Vinyals, O., Liang, P., Dean, J., & Fedus, W. (2022). Emergent
abilities of large language models. arXiv:2206.07682.
Wei, L. (2023). Artificial Intelligence in language instruction: Impact
on English learning achievement, L2 motivation, and self-regulated
learning. Frontiers in Psychology, 14, 1261955.
Zhai, C., & Wibowo, S. (2022). A systematic review on cross-culture,
humor and empathy dimensions in conversational chatbots: The
case of second language acquisition. Heliyon, 8(12), e12056.
Zhang, L., Basham, J. D., & Yang, S. (2020). Understanding the imple-
mentation of personalized learning: A research synthesis. Education-
al Research Review, 31, 100339.
Zhang, S. (2021). Review of automated writing evaluation systems.
Journal of China Computer-Assisted Language Learning, 1(1), 170-176.

150 The Education Revolution through Artificial Intelligence