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|title=Chatbots and English as a Foreign Language Learning: A Systematic Review
|pdfUrl=https://ceur-ws.org/Vol-3667/DC-LAK24-paper-11.pdf
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|authors=Steve Woollaston,Brendan Flanagan,Hiroaki Ogata
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==Chatbots and English as a Foreign Language Learning: A Systematic Review==
https://ceur-ws.org/Vol-3667/DC-LAK24-paper-11.pdf
Chatbots and EFL Learning: A Systematic Review
Steve Woollaston1, Brendan Flanagan2, and Hiroaki Ogata3
1 Graduate School of Social Informatics, Kyoto University, Kyoto, Japan
2 Center for Innovative Research and Education in Data Science, Kyoto University, Japan
3 Academic Center for Computing and Media Studies, Kyoto University, Kyoto, Japan.
Abstract
Chatbots have been increasingly playing a greater role in English as a foreign language education,
offering learners the opportunity to practise with a conversational agent at any time and in different
contexts. To grasp how this field has developed and identify emerging trends and opportunities, we
conducted a systematic bibliometric analysis of research on chatbots in English language learning from
2006 to 2023. The analysis highlights the increasing importance of Large Language Models in language
learning, exploring their potential to overcome previous limitations of chatbot technology. The
implications of these findings for future research are discussed, particularly the potential for chatbot
designs tailored to the specific needs of English language learners.
Keywords
Chatbots, EFL, bibliometric analysis, DB-CALL, LLM, conversational agent, dialogue system 1
1. Introduction
Natural and flowing dialogue is an essential aspect of communication and creating shared
understanding. Simulating dialogue has been one of the goals of researchers since the first
chatbot, ELIZA, was developed more than 50 years ago [1]. Chatbots have exploded in popularity
over recent decades in numerous fields; including customer service, gaming, healthcare, and
education [2]. In the context of second language acquisition, practising dialogue is imperative for
developing natural language skills and communication competence [3]. Conversing with chatbots
is one of the closest approximations there is of conversation with real people. As native and
proficient speakers for conversation practice can be challenging to access due to geographical,
time, and resource constraints, chatbots provide an alternative for practice in a given target
language. Also known as dialogue-based computer-assisted language learning (DB-CALL),
chatbots have shown to have a significant positive effect on the development of second language
proficiency [4]–[6]. In learning English as a foreign language (EFL), chatbots can be used for
conversation practice, roleplay, answering language related questions, conducting assessments,
and providing feedback [7]. Huang et al. [7] identifies three benefits of using chatbots for language
learning: anytime anywhere availability; broad language knowledge; “tireless assistants” when
compared to human counterparts. However, several limitations of chatbots have also been
identified [7], [8]: novelty effects - heightened initial interest, enthusiasm, and engagement due
to the chatbots newness and learner curiosity; formulaic and predictable responses [9]; lack of
personalisation to individual learner needs and localisation issues [10]; lack of contextual
understanding; and technological limitations where unlike humans, chatbots can be more
sensitive to erroneous input (e.g. spelling mistakes). They may also find it difficult to maintain
conversational consistency or stay “on topic” [11].
As there have been many recent advances in chatbot methods and technology, this paper
presents a bibliometric analysis of peer-reviewed research articles from the Web of Science
database on chatbots in the context of second language acquisition. Specifically, this paper seeks
to answer the following research questions (RQs):
1. What are the publication trends in research on chatbots and EFL learning?
2. Who are the key authors and research groups working on and researching chatbots for
EFL learning?
LAK-WS 2024: Joint Proceedings of LAK 2024 Workshops, March 18–19, Kyoto, Japan
s.m.woollaston@gmail.com (S. Woollaston); flanagan.brendanjohn.4n@kyoto-u.ac.jp (B. Flanagan);
hiroaki.ogata@gmail.com (H. Ogata)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
3. What are the milestone articles in the field of chatbots for EFL learning?
4. What are the key themes - current and emerging, challenges, and future directions of
chatbot use in EFL learning?
2. Related work
Several literature reviews and meta-analyses have been conducted in recent years. Pérez,
Daradoumis, and Puig [12] reviewed the literature on chatbots in the field of education. They
found that chatbots are regularly used and effective in education to assist and teach in a large
variety of educational settings, including supporting learning for minority groups and learners
with disabilities. Wollny et al. [13] specifically focused on chatbot applications and their
pedagogical roles, such as: learning, assisting, and mentoring, with four main objectives: skill
Improvement, efficiency of education, students’ motivation, and availability of education. Kuhail
et al. [14] reviewed 36 articles from 2011-2021 and found that over a third of chatbots were for
computer science education. Very few chatbots were used for scaffolding, as motivational agents,
and only two chatbots acted as teachable agents where the learners were tasked with teaching
the chatbot. Most chatbots provided chatbot-driven conversation within a narrow knowledge
domain, and very few allowed user-driven conversations due to the technical complexity of an
open-ended design. However, most experimental studies showed a statistically significant
improvement in “learning and student satisfaction” (p. 1007).
Regarding language learning, Bibauw et al. [5] conducted a meta-analysis of 17 studies on
dialogue-based CALL which showed a significant medium effect of interaction with chatbots on
target language proficiency, and systems that provided corrective feedback were particularly
effective. Zhai [15] reviewed 28 articles published in the last 10 years on AI-based dialogue
systems for improving the EFL interactional competence in university students. They identified
six dimensions, made up of 25 sub-dimensions, that influence the application of chatbots for
learning English: technological integration, task designs, student engagement, learning objectives,
technological limitations, and the novelty effect.
In this paper, we present a systematic bibliometric analysis of the research in chatbots and
EFL learning focusing on recent important advances such as Large Language Models (LLMs), with
discussion on themes and potential gaps in the literature, as future implications for the field.
Advancements in LLMs include language understanding and production by machines, and could
provide solutions to some of the technical obstacles that earlier iterations of chatbot technology
encountered. As many reviews have been published pre-LLMs, this study seeks to explore the
potential of LLMs in this field, and suggest opportunities and potential challenges for the future.
3. Methodology
A bibliometric analysis was conducted to survey and help understand the trends occurring in
chatbot usage in English language acquisition. Bibliometric analysis is an established technique
for systematically quantifying the academic literature on a given topic in a variety of academic
disciplines [16]. These analyses are often used to elucidate emerging trends, analyse article
performance and patterns of collaboration, and also identify potential gaps in the literature [17].
The literature search was conducted in early December of 2023. Biblioshiny, web interface for
the bibliometrix RStudio package, was utilised for the bibliometric analysis [18]. Relevant
keywords were collaboratively chosen by the authors to include all articles that discuss chatbot
usage in English language learning. The authors wanted to include all articles at the intersection
of conversation agents and English language learning. These keywords include common
synonyms for each concept, without introducing excessive unrelated material. After several
iterations, the final search query was the following: (TS=(chatbot*) OR
TS=(conversation* agent) OR TS=("dialogue system")) AND (TS=(language
learn*) OR TS=(language acqui*)) AND (TS=(English) OR TS=(ESL) OR
TS=(ESOL) OR TS=(EFL) OR TS=(EAL)). A search was conducted on Web of Science (WoS),
a platform that provides access to the metadata of high quality academic journals and conference
proceedings via multiple databases. This database was chosen for its interdisciplinary coverage
providing a comprehensive overview of the literature spanning multiple disciplines. WoS is also
well-known for the quality of its sources, indexing peer-reviewed scholarly literature from
reputable journals [19]. A total of 182 records were returned and all fields were exported for
analysis.
To ensure a systematic and transparent review, the PRISMA inclusion/exclusion process was
utilised [20], as shown in Figure 1. PRISMA is a widely recognised method for reporting
systematic reviews and meta-analyses. Of the total 182 records, one was a duplicate, and an
additional 81 were excluded after review. Articles were excluded if they were not related to
chatbots, language learning, or not relevant to either of these. Reviews and meta-analyses were
also excluded. A dual-reviewer approach was employed for the article selection. Initially, two
researchers independently conducted a blind review of the article titles and abstracts. This
independent process ensured that each researcher's evaluation was not biassed by the other.
Following the independent review phase, the two researchers convened to compare and discuss
their findings. There was 98% agreement on which records should be included. Full texts of the
articles in question were retrieved and more thoroughly reviewed until agreement could be
achieved. Various meta data of the 100 articles is shown in Table 1.
Figure 1: PRISMA flow diagram of the review inclusion process
Table 1
Included article main information
Bibliometric Indicator Value
Timespan 2006:2023
Unique sources (Journals, Books, etc) 76
Total documents 100
Annual growth rate % 11.38
Document average age 4.18
Average citations per document 9.87
References 3383
Keywords 559
Total unique authors 248
4. Results
The following section provides the results of the bibliometric analysis and aims to answer RQ1-
3. Table 2 displays the top five journals that have published research articles on chatbots and EFL
learning. Eight articles were published in CALL, a high-quality and prominent interdisciplinary
journal in the field of language education technology. Notably, articles on this topic are being
published in a wide variety of sources.
Table 2
Most relevant publication sources
Publication Source Number of documents
Computer Assisted Language Learning 8
Interactive Learning Environments 5
Applied Sciences-Basel 3
British Journal of Educational Technology 3
Education and Information Technologies 3
Table 3 and Figure 2 show the publication and citation trends for the ten most industrious
countries. Recently, China has shown notable productivity, which may be attributed to
changes in government policy following the international UNESCO conference held in China.
This event culminated in the adoption of the 'Beijing Consensus on Artificial Intelligence and
Education' [21], a framework of guidelines and recommendations designed to maximise the
use of AI in education." Figure 3 is cumulative and is limited to the past ten years to more
clearly highlight the recent uptick in research activity in this space. Globally, research output
has increased more than fivefold in the last three years.
Figure 2: Article publication over time by country (2013 - 2023)
Table 3
Top 10 cited countries and their article publication frequency (2006 - 2023)
Country TC Article Production Frequency
China 314 90
Japan 178 18
USA 101 48
Korea 84 30
Canada 61 9
Iran 53 5
Belgium 40 4
Vietnam 32 2
Greece 26 10
UK 24 10
Figure 3: Collaboration network
To answer RQ2, a collaboration network was generated to analyse the co-authorship
patterns within the dataset. This visualises the connections between authors based on their
shared publications. As illustrated in Figure 3, it is possible to identify several key research
collaborations among these authors.
To answer RQ3, citations were examined more closely. In this dataset, the article by Fryer
and Carpenter [22] is the most cited work by far, as shown in Figure 4 and Table 4. This
prominence is probably because it is one of the earliest studies to use Jabberwacky, a
pioneering chatbot that was a precursor to Cleverbot. Rollo Carpenter, one of the authors of
this paper, developed Jabberwacky. This chatbot was innovative in learning new responses
and contexts from live user interactions, unlike earlier chatbots that relied on fixed databases
[23]. Jabberwacky's advanced capabilities led to it winning the Loebner Prize, a yearly AI
competition where chatbots are judged for their human-like qualities in a Turing test-like
scenario.
Table 4
Top 5 global cited documents (2006 - 2023)
Article Total TC / year Normalised
FRYER L, 2006, Lang. Learn. & Technol. 120 6.32 3.08
JIA J, 2009, Knowl.-BASED Syst. 51 3.19 2.28
BIBAUW S, 2019, Comput. Assist. Lang. Learn. 40 6.67 2.82
WANG YF, 2017, Br. J. Educ. Technol. 39 4.88 2.89
TAI TY, 2023, Interact. Learn. Environ. 39 19.50 8.26
The article by Tai and Chen [24] was actually published online in 2020, so has had some
time to accumulate references. It describes a study on adolescent language learning with
Google Assistant. The researchers found that when interacting with the intelligent assistant
via speech, learners had increased communicative confidence and reduced speaking anxiety.
In 2019, Bibauw et al. [25] conducted one of the most comprehensive systematic reviews
of the literature on DB-CALL to date. From 343 publications, 96 chatbots systems were
identified; their interactional, instructional, and technological traits were analysed. They
summarised empirical studies on their effectiveness in the context of SLA and proposed
several avenues for future research.
Figure 4: Co-citation network (larger font and node size indicate more citations)
To answer RQ4, an analysis was conducted on the keywords provided by each article’s
author(s).
Figure 5 shows the cumulative frequency from 2006 until 2023 of the top 15 keywords in
the dataset. The chatbot keyword in this dataset has exploded in popularity, with a total of 35
cumulative occurrences; 24 of those since 2021. The next most common keyword is artificial
intelligence with twenty cumulative occurrences.
Figure 5: Cumulative word frequency over time of top 15 keywords
Figure 6 visualises the landscape of research within the field of chatbots for EFL learning
as a thematic map. The horizontal axis represents the centrality of themes to the field, with
themes towards the right being more relevant. The vertical axis indicates how developed each
theme is, with higher positions showing greater maturity in research within the dataset.
Chatbot and artificial intelligence are basic themes, suggesting they are very relevant yet still
developing within the field. Predictably, the map also identifies English, conversational agents,
and learners as motor themes, indicating they are both central and highly developed in the
research literature. Niche themes such as English writing are well-developed but less central.
Dialogue systems is situated in the Emerging or Declining Themes quadrant, perhaps as the
term itself is losing favour.
Figure 6: Thematic map of current dataset: 2006 - 2023
5. Discussion
LLMs like OpenAI’s GPTs are transforming the landscape of chatbots and language learning
in general [26]. Utilising their capabilities in natural language processing, advanced dialogue
comprehension and generation, and personalised feedback, LLMs have the potential to
address some of the challenges inherent in traditional chatbots and improve learning
outcomes.
LLMs such as ChatGPT have mainly been applied to language learning as a generic
support tool, with recent research focusing on the effectiveness and affordances as a learning
tool [27]–[29], and perceived usefulness as a learning tool by students [30], [31]. Aspects of
previous research that have investigated integrating LLMs have been limited to the
effectiveness for generating dialogue materials as a chatbot in EFL [32], comparative writing
evaluation with EFL learners [33], and employing it as a tool for automated writing feedback
[34]. As suggested by Fryer and Carpenter [22] in their seminal work, bots are usually
developed to target native speakers and therefore often only effectively cater to the needs of
intermediate to advanced learners. While previous research has often leveraged that LLM-
generated artefacts have strong resemblances to those of native speakers, to the best of the
author’s knowledge there is no research into tailoring LLM output to meet the specific needs
of EFL learners.
Hence, more research needs to be conducted in this area. Depending on one’s theoretical
and pedagogical positioning, an ideal language learning chatbot will: match a learners
language level for each language skill; simulate real-life language and conversation skills;
detect, identify, and correct errors when appropriate for optimal learning; provide diverse,
appropriate, and personalised content; provide a system for tracking progress; identifying
needs, strengths, and weaknesses; foster motivation in learners [35]. This paper proposes the
development of an English language learning chatbot using two techniques to strive for the
ideal described above:
1. Building on the work of Baek et al. [36] where they enhance an LLM’s ability to respond
without additional model training, an LLM could be pre-prompted with pertinent
information about a learner and their language learning needs.
2. Data about the learner - their progress, language learning needs etc. - would need to
be stored and processed to provide the chatbot with relevant pre-prompting. Flanagan
et al. [37] have already made much progress in this space with their work on knowledge
map creation for modelling learning behaviours in digital learning environments.
In Figure 7 we propose a possible chatbot design utilising these two techniques. The
learner converses with an LLM through a personalised and context-aware prompt constructor.
The prompt is generated based on knowledge from the learning content as given provided by
the teacher or course guidelines, chatbot parameters such as explicit instructions on how to
respond to questions to enhance learning, and learner data, such as: English level, past
session data and learner models based on interaction with other learning systems.
Figure 7: Possible configuration of LLM-based chatbot to support language learning
In conclusion, this paper has systematically analysed the intersection between chatbots
and EFL learning in the literature. Specifically, publication trends were examined. Key authors,
research groups, and journals were identified, and milestone articles in the field were reviewed.
Key themes in the space were also investigated, illustrating the recent growth in LLM-based
chatbot technology such as ChatGPT in the literature. The advent of LLMs marks a
groundbreaking advancement in this field, offering more natural context-aware interactions.
Future research should focus on tailoring LLMs to meet specific needs of language learners,
considering aspects like language level matching and personalised content. One possible
configuration for this is described for the wider research community to discuss. As chatbots
become more integrated into language learning and educational programmes, they hold the
potential to enhance how English is taught and learned, making language education more
accessible, engaging, and effective.
Acknowledgements
This work was partly supported by JSPS Grant-in-Aid for Scientific Research (B) JP20H01722
and JP23H01001, (Exploratory) JP21K19824, (A) JP23H00505, and NEDO JPNP20006.
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