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Paper—Teachers’ Views on the Use of Chatbots to Support English Language Teaching in a Mobile…
Teachers’ Views on the Use of Chatbots to Support
English Language Teaching in a Mobile Environment
https://doi.org/10.3991/ijet.v16i20.24917
Kee Man Chuah1,2(), Muhammad Kamarul Kabilan2
1 Universiti Malaysia Sarawak, Kota Samarahan, Malaysia
2 Universiti Sains Malaysia, Penang, Malaysia
kmchuah@unimas.my
Abstract—The development in machine learning has allowed chatbots to be
widely applied into educational settings. However, limited study has investigated
teacher’s views on its usage for teaching and learning. This paper reports an ex-
ploratory study on English as a Second Language (ESL) teachers’ views with
regards to the use of chatbots for their teaching and learning delivery in a mobile
environment. Using survey research design, views from 142 ESL teachers were
gathered using questionnaires, which consist of Likert-scale items and open-
ended questions. The teachers were sampled using purposive sampling method.
The items and questions were developed based on the principles of the Commu-
nity of Inquiry (CoI) framework, which focuses on social, cognitive and teaching
presence. Data from the Likert-scaled items were analyzed using descriptive sta-
tistics while open-ended questions were coded thematically. The findings showed
that teachers perceived the use of chatbots in giving feedback to their students as
very helpful though some of them needed extra training on how to use them. They
also thought chatbots can simulate an interaction cycle for students to practice
the target language. In addition, the teachers believed chatbots augmented a
greater level of social presence, which eventually creates an environment for their
students to be active. All in all, the findings provided valuable insights on the
proper integration of chatbots in teaching and learning while gauging essential
affordances and constraints of its use from ESL teachers’ perspective.
Keywords—Chatbots, ESL teachers, mobile learning, community of inquiry
1 Introduction
The proliferation of mobile technologies for teaching and learning, especially in
English as a Second Language (ESL), has permitted teachers to support their classroom
instructions in many ways. Its application can be identified across all levels of educa-
tion from kindergarten until higher education [1]. Previous studies in the area of mo-
bile-assisted second language learning [2, 3, 4, 5] have shown how mobile tools can
increase opportunities for authentic use of language (e.g., interaction with native speak-
ers, social networking), provide engaging activities that enhance students’ understand-
ing (e.g., game-based tools) and allow seamless access to useful materials and resources
(e.g., open educational resources). Thus, with appropriate pedagogy, mobile tools are
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able to extend the learning of English language beyond the class time and not confined
to textbooks. As stipulated by Chinnery [6], “the effective use of any tool in language
learning requires the thoughtful application of second language pedagogy” (p. 9).
Therefore, any excitement in promoting mobile applications for second language learn-
ing in terms of its affordances or advantages should be viewed from the pedagogical
viewpoint. In addition, Papadakis [7] highlighted the challenge in selecting educational
applications is reducing the focus on entertainment value and prioritise educational im-
pact on learners’ development. In light of this, teachers’ views on how a specific appli-
cation is used is pivotal as they are at the forefront of decision making and designing
learning tasks.
One area in mobile learning that has grown rapidly is the use of conversational
agents or chatbots thanks to the advancement in artificial intelligence. A chatbot is a
computer programme that converses with people on a certain subject or area in a natu-
ral, conversational manner utilising text or speech [8]. Chatbots have been used for a
variety of reasons in many areas, including marketing, customer service, technical as-
sistance, education, and training [9]. The origin of chatbots can be traced as far back as
the late 1960’s when ELIZA was introduced – a simple bot that gives responses based
on keywords inputted by users [10]. Now, it is more human-like and accepts more than
just text inputs. From virtual assistants (e.g., Siri, Google Assistant) to text-based chat-
bots that can be installed in chat apps (e.g., Telegram, Discord, Facebook Messenger).
Many studies related to chatbots were mainly in terms of its development algorithms
[11] or for general educational use [12]. Studies on the use of chatbots in English lan-
guage learning, however, have focused on three major areas.
The first area is in terms of the general provisions on how chatbots can be integrated
in language teaching and learning. Dokukina and Gumanova [13] highlighted the role
of chatbots in creating natural language interaction, which allows learners to use the
target language in simulated contexts. Wu and Yan [14], on the other hand, proposed
several deep-learning mechanisms that could enhance the interactivity of chatbots in
order to make them closer to features of human interactions. Despite the initial chal-
lenges in integrating chatbots for language learning, Fryer et al. [15] showed optimism
that chatbots will begin to dominate language learning especially in informal settings.
They argued that the development in machine learning has allowed chatbots to function
more effectively for educational purposes. Their empirical study has also indicated lan-
guage learners’ preference to practise language with chatbots as the absence of a human
instructor reduced their fear of making mistakes. Echoing the same view, Alm and
Nkomo [16] reported that language learners were more willing to engage in conversa-
tion with chatbots though they showed frustrations when the conversations did not
match their learning goals. However, it is worthy to note that their study was focusing
on learners’ own initiative to learn the target language through chatbots without
teacher’s interventions.
The second area of concern is the linguistic accuracy of chatbots. Coniam [17] eval-
uated the performance of five chatbots for English language learning. Three of the five
chatbots had favourable acceptability figures in the range of 90% in terms of grammar
but all five struggled in the semantic or pragmatic part in which many meaningless
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responses were generated. The study reveals the shortcomings in the current implemen-
tation of chatbots that are still highly dependent on keyword detection with minimal
efforts to consolidate changes contextualized meaning. Das and Kumar [18] also stip-
ulated that it is useful to ensure the accuracy of chatbots in providing information for
better user experience. Although chatbots may not be able to converse with human-like
competence, its accuracy and consistency in providing word-level drills and practice is
undisputed. Vanjani et al. [19], for example, demonstrated how chatbots could be used
to learn phrases and vocabulary through a multilingual chatbot that is capable of real-
time translation.
The third area, which has been studied is about the effectiveness or usability issues
of chatbots. Kim [20] conducted an experimental study on two groups of Korean EFL
learners and found that both groups (with and without chatbot intervention) showed
significant improvement in listening and reading skills. However, the group with chat-
bots showed more improvements in the post-listening test. They demonstrated the po-
tential use of Elbot in enhancing learners’ engagement on the use of English. Patrovic
and Jovanoic [21] shared their review of four chatbots and explained the promising
usage of chatbots for personalized language learning. A review by Smutny and
Schreiberova [22], on the other hand, outlined the affordances and constraints of chat-
bots, focusing more on what each bot is able to provide but not specific to English
language teaching.
In essence, the review of previous studies shows that very few studies have been
done to gather the end-users’ opinions of chatbots. In the context of language learning,
the key end-users are teachers and students. While there are studies that examined stu-
dents’ perception [23, 24], those that focused on teachers remain scarce. Since teachers
are the catalyst of any pedagogical intervention, it is pivotal to understand their views
on tools that they can use in teaching and learning. The research done by Chocarro et
al. [25] focused on the teachers’ attitudes towards chatbots from the Technology Ac-
ceptance Model (TAM) point of view but were on chatbots not specific to language
learning. Nevertheless, they provided key insights on the potential use of chatbots in
educational settings as most studies were on the role of chatbots in customer relations
and online business platforms.
In addition, the review reveals that it is rather apparent that ESL teachers’ adoption
of chatbots for English language teaching has not been examined thoroughly. Existing
studies were mostly on general usability issues with little emphasis on uncovering ESL
teachers’ views on its use for English language teaching. It is also important to consol-
idate teachers’ view on the use of chatbots through a theoretical framework related to
online learning environment such as Community of Inquiry by Garrison et al. [26] in
order to assess its potential as a teaching tool. By using the appropriate framework,
future integration of chatbots in English language teaching and learning can be done
more systematically. Hence, this study aims to answer the following research questions
(RQ):
1. What are the ESL teachers’ views on the use of chatbots in English language teach-
ing through a mobile environment?
2. To what extent can chatbots support the Community of Inquiry among the learners?
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The following section of this paper describes the theoretical underpinnings that guide
this study, particularly with regards to why chatbots are used to support English lan-
guage teaching in a mobile environment. This is then followed by the explanation of
methods used. Next, the results and discussion are provided in accordance with the
research questions. This paper ends with the conclusion section that summarises the
key results while highlighting their implications and suggestions for future research.
2 Theoretical underpinning
To guide the investigation on ESL teachers’ views on the use of chatbots for lan-
guage learning, the Community of Inquiry (CoI) framework as proposed by Garrison
et al. [26] was used. Being a framework inspired by constructivism, CoI focuses on
learning that spawns from experience that is contextualised and socially situated [27].
Furthermore, CoI was formulated with a specific focus on computer-mediate commu-
nication (CMC), which is the relevant to this exploratory study as chatbots were used
to assist teachers in CMC. Previous studies on CoI have shown its usefulness in assist-
ing researchers to identify affordances and constrains in various CMC platforms such
as discussion forums and chat channels [28, 29]. Qin et al. [30] also demonstrated how
a chatbot was developed using CoI and showed that features within the chatbot can
address the presences that CoI aims to create in a learning environment. Nevertheless,
their study was on students’ perception and regarding a general topic, which is not re-
lated to language learning. Thus, there is a gap in further investigating how a mobile
learning environment created based on chatbots can be helpful in creating all the
presences within the CoI framework.
Fig. 1. The Community of Inquiry framework (Source: https://courses.dcs.wisc.edu/)
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Specifically, the emphasis in this study is placed on how chatbots (regarded as a
virtual human-like assistant) can simulate a learning environment that fosters social,
cognitive and teaching presences. As explained in [26], social presence covers the af-
fective part of the learning environment as to whether teacher and students are able to
build a trusting environment that fosters interpersonal relationships. Cognitive pres-
ence, on the other hand, refers to the ability of the learners to construct and confirming
meaning by constant reflection or engaging in peer-to-peer discourse. In other words,
cognitive presence is related to cognitive engagement in which students are being
guided into deep learning rather than merely accepting what is conveyed. Lastly, teach-
ing presence refers to the facilitation, direction of cognitive and social processes that
lead to intended learning outcomes.
This framework is chosen due to its relevance in online learning, particularly in asyn-
chronous contexts. Since chatbots, in this study, were used as supplementary activities
or tasks in a mobile learning environment, there is a tendency for the chatbots to be
used in asynchronous mode, much like a discussion forum. Although chatbots can offer
spontaneous feedback, learners may take time to respond since the responses are text-
based, are not being put under pressure to respond immediately. Therefore, it gives a
practical sense to investigate the chatbot usage among ESL teachers using this CoI
framework.
3 Methods
A survey research design was used as the purpose of this exploratory study is to
identify the views of ESL teachers on the use of chatbots to support their teaching.
Online questionnaires with 5-point Likert-scale items (1 being strongly disagree, 5 be-
ing strongly agree) and open-ended questions were constructed based on the selected
CoI framework. The questionnaire contains three main parts.
The first part is on the participants’ general info such as gender and teaching expe-
rience. The second part contains 10 Likert-scale items and 1 multiple-choice question.
Ten Likert-scale items for the questionnaire were derived based on three main con-
structs namely language learning, feedback to learners and general usability. The items
for language learning (n=4) aim to identify teachers’ views on the potential use of chat-
bots in assisting their students to learn the target language through the simulated social,
cognitive and teaching presences as indicated in CoI. The items for feedback to learners
(n=3) are to check teacher’s opinions on the chatbots’ ability to provide real-time feed-
back especially during their absence. Feedback is seen as crucial in creating an envi-
ronment that supports cognitive and teaching presences as it can facilitate discourse and
assist learners to move from triggering events to at least exploration stage. The usability
items (n-3) are to find out teachers’ views on the convenience of using the chatbots.
The last question in the second part is teachers’ overall view on the affordance of chat-
bots in addressing the three presences in CoI. The third part of the questionnaire con-
tains open-ended questions on suggestions for the integration of chatbots in English
language teaching based on the CoI presences as well.
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3.1 Sample
Through purposive sampling, invitations to ESL secondary school teachers in Ma-
laysia were sent out via email and chat groups. 154 ESL teachers signed up for the study
but only 142 of them managed to provide complete responses to the survey, giving a
response rate of about 92%. The gender distribution is almost equal. The criteria set for
the sampling ensured that they are all secondary school teachers with at least three years
of teaching experience and are familiar with the use of mobile tools for learning. The
primary reason for these criteria is to make sure the teachers were comfortable in using
technology and had sufficient experience in lesson design. Also, in the context of the
study, the use of mobile tools at the secondary school level is permitted and this makes
it easier to manage than primary school students due to students’ level of maturity.
Table 1 shows the demographic information of the selected sample.
Table 1. Demographic information of the sample
Information
Categories
Frequency
Percentage
Gender
Male
67
47%
Female
75
53%
Teaching
Experience
3 to 5 years
56
39%
6 to 10 years
38
27%
10 to 15 years
30
21%
More than 15 years
18
13%
3.2 Data collection procedures
Prior to data collection, the teachers who signed up for the study were specifically
briefed on the three core components of CoI so that they were aware of the key elements
in each presence. The briefing was done via video-conference platform (Google Meet)
in several sessions according to the teachers’ choice of slots. Table 2 shows examples
of chatbot prompts for each corresponding component of CoI, which were explained to
teachers. This briefing is important in ensuring the data gathered from the questionnaire
are valid and reliable since all teachers must be able to differentiate the CoI presences.
Table 2. Example of chatbot prompts according to CoI presences
Presences
Categories
Example of Chatbot Prompts
Social
Personal or affective/emotion
“Hello 😊, what do you want to learn today?
Open Communication
“You are right, David. Sturdy means strong”
Group Cohesion
“Let’s do this together”
Cognitive
Triggering Event
“Do you know the meaning of sturdy?”
Exploration
“Can you think of other examples?”
Integration
“Try relate this to your friends”
Resolution
“I am not sure, could you check it up?”
Teaching
Design and organisation
“In Part 1, there are 10 words”.
Facilitating discourse
“Now, we’re going to learn about food”
Direct instruction
“Please explain what do you mean”
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In collecting the data, teachers were required to use chatbots as part of their mobile
learning activities for about two months. Prior to designing the mobile learning tasks,
the teachers were also required to use the chatbots in order to identify the features avail-
able. Some example tasks were also given to teachers as guidance. For example, stu-
dents can be told to complete a portfolio of weekly logs with the chatbots on several
topics. The two chatbots selected for this study are shown in Fig 2. These chatbots can
be integrated into Telegram and Facebook Messenger and are related to English lan-
guage learning. As the teachers have been using Telegram to manage their classroom,
it makes it more convenient for teachers to directly introduce them to the students with-
out the need for extra installation. After two months, the teachers were required to com-
plete the survey questionnaire, which was administered online for a period of one week.
Fig. 2. Screenshots of the selected chatbots
3.3 Data analysis procedures
The gathered data from the Likert-scale items were analysed using descriptive sta-
tistics in which mean and standard deviation values were computed and tabulated. Since
this was an exploratory study, only ten key items were used and analysed. As for the
item on teachers’ overall view of CoI presences, it was quantified using frequency count
and percentages as teachers can select more than one presence. For the open-ended
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responses on suggestions for integration and improvements, the gathered data were an-
alysed using thematic analysis where each response was read and coded according to
major themes.
4 Results and discussion
This section presents the results obtained from the study based on the research ques-
tions. The results are also discussed in relation to pertinent literature.
1. What are the ESL teachers’ views on the use of chatbots in English language teach-
ing through a mobile environment?
2. To what extent can chatbots support the Community of Inquiry among the learners?
4.1 Teachers’ views on chatbot integration
Table 3 shows the overall means score of items concerning ESL teachers’ views on
the use of chatbots (RQ1) after integrating them for about two months.
Table 3. Mean scores for items related to teachers’ views on chatbots integration
Construct
Item
Mean
SD
Language
The chatbots simulate authentic language use.
4.13
0.981
The chatbots model good use of words or phrases.
4.03
0.756
The chatbots produce accurate language use.
3.79
0.886
The chatbots model good use of grammar.
3.67
0.672
Feedback
The chatbots help to provide immediate feedback.
3.92
0.734
The chatbots allow students to do self-checking.
3.88
0.708
The chatbots correct students’ mistakes directly.
2.92
0.784
Usability
The chatbots are easy to be used.
4.34
0.678
The chatbots have a friendly interface.
4.09
0.813
The chatbots load smoothly through Telegram/ Messenger
3.96
0.871
The ESL teachers were positive about the use of chatbots in their teaching but re-
mained reserved on chatbots’ accuracy as shown in the lower mean score for grammar.
On the whole, the items were rated highly with strong agreement on the usefulness of
chatbots in simulating authentic language use (mean=4.13) and model of good use of
words and phrases (mean=4.03) as well as grammar (mean=3.67). The simulation of
authentic language use is largely due to the prevalent social presence when chatbots are
used. Since chatbots are “human-like”, the conversations are often filled with social
cues and emotions (e.g., the use of emoji). This type of environment encourages learn-
ers to be more comfortable and more willing to communicate as indicated in the study
by De Cicco et al. [31].
It is also important to note that despite chatbots being able to respond immediately
(mean=3.92), the teachers found that some of the corrective feedbacks were not mean-
ingful. This is reflected in their low mean score (mean=2.92) for the items on correcting
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students’ mistakes. Despite this problem, the teachers seem to agree that the chatbots
could build cognitive presence as students were able to learn some good usage of Eng-
lish on their own and perform necessary evaluation on the responses provided by the
chatbots (exploration and integration in cognitive presence). This finding reflects the
concerns raised by Coniam [17] and Das and Kumar [18] in which the accuracy of the
chatbots could be the factor that hinder its continuous use in teaching and learning.
However, as stipulated by Qin et al. [30], this problem can be overcome when teachers
learn to design their own chatbots and use accurate language input to guide learners.
In terms of usability, the teachers also believed that the chatbots are easy to use
(mean=4.34) and they had no difficulty introducing them to the students during their
teaching and learning activities. One reason for this could be the selected chatbots in
this study were capable of being embedded within existing chat applications (i.e., Tel-
egram and FB Messenger) and do not require complex installation. These findings ech-
oed what was found in [13] as teachers explore the use of chatbots to assist them in
providing more opportunities for language use though improvements are necessary to
increase the chatbots’ accuracy.
4.2 Teachers’ views on the role of chatbots in supporting presences in CoI
The teachers were also asked to indicate their views on the three presences (RQ2)
explained to them, as to whether chatbots can simulate a learning environment that
contains those presences. Table 4 shows their overall view.
Table 4. Teachers‘ views on the roles of chatbots in simulating presences in CoI
Presence
Count
Percentage
Social presence
122
86%
Teaching presence
107
75%
Cognitive presence
68
48%
Clearly, social presence is regarded to be dominant while cognitive presence is lack-
ing since the chatbots were not intelligent enough to prompt the students to engage in
deeper reflection. As found in the study by Huang et al. [32], social presence was highly
rated among their participants as the chatbots were able to interact with them without
fail though there were instances when they did not understand what the bots were say-
ing. It was still capable of creating a virtual setting for interpersonal interactions that
allowed learners to practise using the language.
It is interesting to note that teachers accepted the role of chatbots in creating teaching
presence particularly in situations where they could not make themselves available. For
example, when students are at home, it would hard for teachers to monitor them pro-
gressively especially when the students would like to ask questions pertaining to word
usage. It can be assumed that chatbots can act as an assistant to teachers in dealing with
trivial questions. The teachers can focus more on important corrective feedback to the
learners. As reviewed by Smutny and Schreiberova [22], most chatbots are still not able
to engage in meaningful conversation beyond the keywords set for each bot. Therefore,
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teacher intervention is still needed in ensuring the learners are getting accurate feed-
back.
4.3 Teachers’ suggestions for improvement
Additionally, to supplement the results for RQ2, the teachers were asked to provide
suggestions on the kind of improvements that can be done on the chatbots in accordance
to the three presences in CoI. Three recurring themes emerged as shown in Table 5.
Table 5. Emerging themes and responses form open-ended questions
Themes
Some Extracted Responses
Presences
Improvements in terms of
artificial intelligence
The chatbots need to improve its responses. It seems not so
intelligent.
Cognitive
I find the chatbots rather mechanistic after a few rounds of
usage. My students complained the same. Need to improve
this to make social presence seems more natural.
Social
Accuracy of the corrective
feedback
It is very useful since students can learn the language rather
authentically, but the accuracy can be improved.
Teaching &
Cognitive
My students love it since they don’t have to ask me regu-
larly. But some responses are not accurate. I think maybe
need to check on this.
Cognitive
Collaboration
It would be good if the chatbots can also support group
chats. This would be useful for discussion-like tasks.
Social
My students wanted to work together, so I had to design a
task where they get to discuss but respond to the chatbots to-
gether. Can add this feature.
Social
Lesson Design
Since the tasks were mainly on students talking to chatbots, I
think if we can design other types of activities would be in-
teresting
Teaching
Cognitive wise, it reaches maybe up to Comprehension in
Bloom’s Taxonomy or exploration stage in the framework
you mentioned. But I like how chatbots become like a social
partner encouraging my students to use the language more.
Pretty brilliant.
Cognitive &
Social
In general, the teachers believed that the responses provided by the chatbots could
be made more intelligent so that it covers a more natural discourse that can be used to
encourage continuous conversations. The issue of accuracy in guiding the learners is
also prevalent in which the bots are designed to deal with keyword-based responses
[30]. Some of the responses were not meaningful and caused confusion among the
learners. This seems to affect the teaching and cognitive presences. There are also sug-
gestions for chatbots to include a collaborative feature whereby group conversations
can be made as this feature is capable of alleviating social presence. The feature would
also be helpful in the ESL context since many task-based activities require group dis-
cussions. Moreover, the teachers provided suggestions in terms of lesson design. They
felt that chatbots can be better integrated into English language teaching if a variety of
tasks could be designed. They value the social presence that chatbots create but believe
that it would be beneficial in simulating higher cognitive presence. This suggestion is
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in line with the study done by Go and Sundar [33] whereby human-like chatbots would
increase the level of interactivity. Nevertheless, the overall feedback from the open-
ended questions still indicates a strong acceptance among the teachers with regards to
chatbots being their assistant in guiding the learners. Since one of the challenges in ESL
context is to encourage learners to use the target language, chatbots are seen as a good
tool to support teachers. Learners can engage in conversations that highlight how spe-
cific words or phrases are used in a friendlier mode.
5 Conclusion
The exploratory study has provided insights into ESL teachers’ views related to the
use of chatbots for English language teaching in a mobile learning environment. Similar
to studies in mobile learning [34, 35, 36, 40], the findings from this study have shown
ESL teachers’ willingness to adopt mobile tools to enhance the learning experience.
There is a huge potential of using chatbots to encourage ESL students to actively use
the target language as ESL learners tend to lack the chance to use them at home [37].
The findings serve as a foundation on how to integrate the use of chatbots in English
language teaching by taking into consideration the views of ESL teachers. They have
shown interest in continuing the use of chatbots in designing their teaching and learning
activities despite the problem with the accuracy in the corrective feedback provided by
the chatbots.
This study has contributed to the related body of research pertaining to the Commu-
nity of Inquiry framework by examining its potential uses in chatbot interactions within
the context of English language teaching in a mobile environment. As reported in pre-
vious studies [28, 29], the CoI framework is commonly applied for discussion forums
or asynchronous CMC but in this study, it was used to investigate the conversations
between human and non-human virtual agents. From the methodological perspective,
this study has shown that conversations that take place via chatbots can be examined
by the CoI framework in order to uncover the levels of social, cognitive and teaching
presence. The results have indicated how chatbots could play a crucial role in increasing
social and teaching presences. In relation to teaching presence, the chatbots have served
as a facilitator or teaching assistance to guide the students. It creates an environment
where students feel that they are being scaffolded in learning the target language. With
regards to social presence, chatbots are capable of simulating human-like social inter-
actions, which make the students feel at ease during the learning process. However, it
is important to note that although there are constraints in encouraging its use to simulate
cognitive presence, the teachers still view chatbots to be useful in motivating the stu-
dents to be more actively involved in knowledge exploration and integration. For ex-
ample, whenever they encounter feedbacks which are inaccurate, they would cross
check and reflect on those errors. Such reflective activity is an example of chatbot’s
capability in creating cognitive presence [26]. Interestingly, ESL teachers regard chat-
bots’ social presence can build students’ confidence in practising the correct usage of
the English language though the cognitive presence is minimal. As shown in this study,
ELS teachers have mentioned how students were more proactive and confident in using
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English in the conversations with chatbots. Thomas [38] reported a similar trend in his
study on EFL learners in which there was a weak positive relationship between critical
thinking and achievement emotions. As such, despite the lack of cognitive presence,
social presence can be useful in motivating learners to perform better in a specific aca-
demic task.
In the aspect of practical implications, chatbot developers could take note of the ar-
eas for improvements as suggested by the teachers. One suggestion that was repetitive
is in enhancing chatbots’ ability to provide meaningful feedback with minimal inter-
vention from the teachers. Since chatbots are playing the role of a facilitator, the accu-
racy of their feedback would avoid students from learning the wrong input. In addition,
teachers and instructional designers could also identify elements within the CoI frame-
work that should be given more emphasis when integrating chatbots into their ESL
lesson designs. As emphasised by Mahzan et al. [39], understanding ways to accom-
modate cognitive and emotional needs of ESL learners is useful in boosting learning
engagement.
Although this study is exploratory in nature, it has uncovered some areas for further
investigation. Firstly, the present study did not measure students’ performance in lan-
guage gain or production as the aim was to investigate ESL teachers’ views on the use
of chatbots in supporting their lessons. Thus, it would be beneficial for future research
to measure ESL students’ performance and correlate the data with the teachers’ use of
chatbots in the language learning activities by establishing the connection with the CoI
framework. It would also be beneficial to include variables such as teachers’ and stu-
dents’ technological readiness in the experiments. Secondly, this study is limited to
only two text-based chatbots as they are more convenient to be integrated into the ex-
isting chat apps that the participants were using. It has confined the scope of language
learning mainly to reading and writing skills. Thus, future research can include voice-
based chatbots for comparative analysis as the advanced chatbots could engage in audio
conversations that could be used for students to practise listening and speaking skills.
A comparative study between text-based and voice-based chatbots can potentially yield
interesting results in informing their effectiveness for language learning especially in
ESL contexts. With these suggestions, it is hoped that future studies within the same
scope can further inform the use of chatbots for language learning purposes.
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7 Authors
Kee Man Chuah is a senior lecturer at the Faculty of Language and Communica-
tion, majoring in educational technology, computational linguistics, learning analytics
and instructional design. He has won several awards at national and international levels
for various innovations in teaching and learning as well as assistive technology.
Muhammad Kamarul Kabilan is a Professor at the School of Educational Studies,
Universiti Sains Malaysia, Penang. His research interests include ICT and English Lan-
guage Education and, professional development and critical practices of teachers. He
has published widely in his area of research in reputable journals both locally and in-
ternationally (Email: kabilan@usm.my).
Article submitted 2021-06-19. Resubmitted 2021-08-05. Final acceptance 2021-08-05. Final version pub-
lished as submitted by the authors.
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