Teacher Attitude towards Use of Chatbots in Routine Teaching

Abstract

Teacher's attitude towards some particular technology influences their willingness to use that technology in their instructional processes, and consequently the attitude and responses of students to the technology that they observe teachers using. This has a direct bearing on whether or not such technology will successfully be integrated into routine classroom practice and whether benefits of using such technology for teaching-learning purposes will be realized. This study sought to ascertain the attitude of teachers towards use of chatbot technology for teaching and learning purposes, chatbots being yet an emerging educational technology within a majority of developing countries including Kenya.

Full text

Universal Journal of Educational Research 6(7): 1586-1597, 2018 http://www.hrpub.org
DOI: 10.13189/ujer.2018.060719
Teacher Attitude towards Use of Chatbots in
Routine Teaching
Bii P. K1,*, J. K. Too2, C. W. Mukwa2
1Department of Mathematics and Computer Science, University of Kabianga, Kericho, Kenya
2Department of Curriculum Instruction and Educational Media, School of Education, Moi University, Kenya
Copyright©2018 by authors, all rights reserved. Authors agree that this article remains permanently open access under
the terms of the Creative Commons Attribution License 4.0 International License
Abstract Teacher’s attitude towards some particular
technology influences their willingness to use that
technology in their instructional processes, and
consequently the attitude and responses of students to the
technology that they observe teachers using. This has a
direct bearing on whether or not such technology will
successfully be integrated into routine classroom practice
and whether benefits of using such technology for
teaching-learning purposes will be realized. This study
sought to ascertain the attitude of teachers towards use of
chatbot technology for teaching and learning purposes,
chatbots being yet an emerging educational technology
within a majority of developing countries including Kenya.
Keywords Teacher Attitude, Educational Chatbot,
Educational Software, Chatbot, Chatbot Technology,
Educational Technology, Integration, Social
Constructivism
1. Introduction
Chatbots are a group of computer programs that are
deliberately designed to be social and interactive in nature.
Their goal is to simulate intelligent human language
interaction through text or speech through engaging in
informal chat communication between a human user and a
computer using natural language [1-3]. The first chatbot,
named ELIZA, was created by Joseph Wiezenbaum of
MIT to emulate a psychotherapist in clinical treatments [4].
This was followed by chatbot PARRY developed by
Kenneth Colby of Stanford University in 1972 to simulate
a paranoid schizophrenic [3]. In 1995, Richard Wallace of
Carnegie Mellon University developed the chatbot ALICE
(Artificial Linguistic Internet Computing Entity), based on
AIML (Artificial Intelligence Markup Language), which is
designed to converse with a user on almost any topic of
interest [4]. The technology behind ALICE and AIML was
released in 2001, and this led to the implementation of
various general conversational chatbots based on AIML.
The chatbots generally available are internet-based and are
mostly used for non-instructional purposes as the sample
chatbot summary in Table 1 [5-8] shows.
As an educational technology, chatbots can potentially
be used in a wide variety of ways in instructional settings.
Kowalski, Hoffman, Jain & Mumtaz [7] note that they can
play a useful role for educational purposes, because they
are an interactive mechanism as compared to traditional
e-learning systems. They allow continuous student
interaction by enabling them to ask questions related to a
specific field. However, they go on to add that their use for
instructional purposes is still limited. Jia & Chen [9] in a
study investigated how a chatbot could be used to motivate
learners to practice English. The chatbot used was
web-based and in the study they reviewed free internet
usage of the chatbot over a six-month period. Additionally,
the study evaluated the integration of the chatbot into
English instruction in a high school classroom over a
school term. Among the results of their study were the
findings that students feel the approach can help with
course unit review, make them more confident, improve
their listening ability, and enhance interest in language
learning. Investigations outside language speaking and
learning are far more limited. Kerfoot, Baker, Jackson,
Hulbert, Federman, Oates & DeWolf [10] described an
experiment in which chatbots were used in the training of
medical students. The benefits of use of their web-based
chatbots in teaching were significant increase of test scores
in four topics and a three-fold increase in learning
efficiency. Knill, Carlson, Chi and Lezama [11]
investigated the use of a chatbot called Sofia in the
teaching and learning of Mathematics with their conclusion
being that a chatbot adds to the variety of tools available for
student instruction.

Universal Journal of Educational Research 6(7): 1586-1597, 2018 1587
Table 1. Sample Chatbots and Their Communication Modes
General Chatbots Application Communication Mode
ELIZA Programmed to act as a Rogerian therapist
Input: Textual mode
Output: Textual mode
Jabberwacky Teachable chatbot
Input: textual mode
Output: textual and
Spoken mode.
Jenny General wide vocabulary, replies often out of context Textual mode
Sanelma
A fictional person to talk with in a museum, which provides
background information concerning a certain piece of art.
Textual mode
PC Therapist
Simulate a Rogerian therapist, inspired from ELIZA. Different
personalities have been developed such as: PC professor
discusses men versus women; PC Politician discusses Liberals
versus Conservatives.
Spoken mode
Marloes A female Dutch financial advisor. Spoken mode
MIA A German advisor on opening a bank account. Textual mode
Cybelle
A female avatar with body and uses gestures while talking. She
directs you to discover the agent land, a new land where you can
find more information about agents, what they are, how they
work, how they could be useful for you.
Textual mode
Ultra Hal
Programmed to learn by statistically analyzing
past conversations to determine the most appropriate
response
Supports a number of
speech and graphics engines and will
operate on the Web, and on
Windows, iPhone, Second Life,
Twitter, and Facebook.
Pixel
An AIML chatbot written to answer general questions about the
library and helps users at the University of Nebraska–Lincoln
Libraries
Input: textual mode
Output: Textual mode
Educational
Chatbots
Application
Communication
Mode
Dave
The A. I. Chat Robot DAVE the English Teacher replies in
perfect English just like a private English teacher or human chat
partner. With tens of thousands of words in its vocabulary, he is
the perfect private tutor.
Input: textual mode
Output: spoken and
textual mode.
Speak2Me A female chatbot that is used to teach English language through
chatting.
Input: textual mode
Output: spoken and
textual mode.
Percy Computer Science Teaching Assistant Textual mode
Virtual Patient bot
(VPbot)
Medical students education bot Textual mode
Sources: [5-8]
As of now, not much research has been conducted in
developing countries to uncover specific ways of use of
chatbots in classrooms, what the key players of teachers
and students think of the technology, and actual benefits of
use, though research on uses of computers in instruction,
use of chatbots in some instructional contexts, and specific
use of chatbots in the teaching of English in non-English
speaking countries (also to a limited extent) is extant
[12-14]. There is then a need for systematic integration,
application and evaluation studies to widen findings and
scope. A majority of the aforementioned studies also do not
consider chatbots from the teacher’s perspective.
Investigation of the various factors and dimensions
affecting teachers with regard to use of chatbots in teaching
is important since teachers are an indispensible part of the
teaching-learning environment. One such dimension
pertaining to the teacher is their attitude towards use of
chatbots in their teaching activities. This study therefore
sought to determine the attitude of teachers towards use of
chatbots in teaching in two randomly selected secondary
schools within Buret District, Kericho County, Rift Valley
Province, and the Republic of Kenya. It was undertaken
within the context of a broader study that sought to
establish the effect of use of chatbot technology on
interaction and collaboration patterns in teaching and
learning undertaken from a social-constructivist point of
view. Within the Kenyan context, Nchunge, Sakwa &
Mwangi [15, p17] noted that ‘while there is a wide range of
innovations in ICT to support effective and quality delivery
of education services and curricula, there is a considerable
technology lag in educational institutions. Most institutions
still use nearly obsolete systems and are consequently
unable to exploit the educational potential of the emerging
technologies’. The attitude of teachers towards
technologies that they are encouraged and expected to use
in their teaching is critical, since this influences their

1588 Teacher Attitude towards Use of Chatbots in Routine Teaching
willingness to actively use them in routine teaching and
transfer the same enthusiasm of use to the students under
them [16, 17]. Their attitude also influences their rate of
adoption of emergent technologies that can be of use in
educational settings [18 - 21]. Fu [22] noted that teacher
attitudes toward technology are significant predictors of
teacher and student technology use, as well as of their use
of a variety of instructional strategies.
The chatbot used in the study is named Knowie. This
chatbot was derived by the author from the open source
chatbot Howie originally created by Stratton [23], which
the author downloaded and modified for educational
research and application purposes. The chatbot was
programmed using the Python programming language,
with its knowledge base being implemented using AIML.
Several approaches can be used to add knowledge to a
chatbot. These approaches include starting with an empty
database to which content is automatically added as the
chatbot is used, having the chatbot designer program the
database so that it has pre-programmed questions, phrases
or words and how it is to respond to each question, phrase
or word, and enabling the chatbot to learn from text corpora
[24]. The approach of starting with an empty knowledge
base was implemented in the study, with the additional
provision that teachers and students could deliberately add
knowledge content to the chatbot through direct entry of
question-answer pairs, direct entry of keyword-definition
pairs, and typing of class notes into the bot for later
keyword-definition pair searches and hence automatic
generation of AIML pattern-template pairs for the bot’s
knowledge base. This approach of starting with an empty
knowledge base was deliberately used in order to offer an
opportunity for teachers to implement a
social-constructivist teaching-learning environment with
elements of social context, enhanced social interaction,
collaboration, mediation and scaffolding [25] as per
social-constructivist principles.
2. Research Methodology
2.1. Research Design
The methodology used in the study was mixed methods,
more specifically, a repeated treatment quasi-experimental
case study. Creswell [26, p.1] states that in a case study, the
‘researcher explores in depth a program, an event, an
activity, a process, or one or more individuals. The case(s)
are bounded by time and activity, and researchers collect
detailed information using a variety of data collection
procedures over a sustained period of time’. Further,
Savenye & Robinson [27] note that researchers often
conduct a case study to learn more unobtrusively about
students, teachers, and trainers who use a new technology
and that case studies present detailed data that create a
picture of perceptions, use, attitudes, reactions, and
learner/teacher environments. According to Zainal [28, p2],
‘case studies, in their true essence, explore and investigate
contemporary real-life phenomenon through detailed
contextual analysis of a limited number of events or
conditions, and their relationships’. Hence it was an
appropriate approach to use in this study involving chatbot
technology in a teaching-learning environment. Anwar
Sheik & Bibi [29] add that in it, an individual or an
institution is studied in a unique setting or situation in an
intense and detailed manner for long a period of time. A
chatbot was installed in the computer laboratory in each of
two randomly selected schools offering computer studies
as an examination subject. Ten Form two teachers teaching
these classes were then trained in the use of the chatbot in
teaching. They subsequently trained the students to use the
chatbot after which it was used for teaching-learning
activities for 20 weeks (5 months) spread over two terms of
the school year.
The design used in the study was quasi-experimental and
structured to offer an opportunity to elicit quantitative data
on teacher attitude obtained through questionnaire
administration to teachers who participated in the study. In
a quasi-experimental design, a programme or policy is
viewed as an intervention in which a treatment –
comprising the elements of the programme or policy being
evaluated – is tested for how well it achieves its objectives,
as measured by a pre-specified set of indicators [30].
Quasi-experimental approaches are frequently used when it
is not logistically feasible or ethical to conduct a
randomized controlled trial [31]. The study specifically
purposed to institute instances of chatbot technology use in
two randomly selected school settings by two given classes
and their teachers and then determine the attitude of
participant teachers towards the use of the chatbot in
instruction through a questionnaire administered after their
experience of chatbot use in teaching-learning. According
to Albirini [18, p375], ‘a new technology will be
increasingly diffused if potential adopters perceive that …
the innovation can be experimented with on a limited basis
before adoption’. This is based upon the Trialability
attribute of the technology as given by Rogers [32] and
which influences the technologies’ acceptance and
subsequent adoption. Further, Perkins [33] noted that a trial
period for an innovation will help potential adopters
answer questions that they may have about that innovation.
This study sought to offer participant teachers such an
opportunity for experimentation over a period of five
months spread over the first and second term of the school
year, third term usually being occupied by National Exams
in Kenyan Schools. It is beneficial for teachers to not only
see how technology can be used to support and extend
traditional pedagogy (thereby alleviating resistance to
adoption), but also to see how technology can be used to
create richer, more engaging, student centered approaches
to knowledge acquisition by students, rather than limiting
their use to notes preparation, tests preparation, and

Universal Journal of Educational Research 6(7): 1586-1597, 2018 1589
classroom management [22].
Questionnaire use for data collection has a number of
advantages including affordability, ability to reach a large
number of potential respondents in a limited time,
efficiency, and standardization though it has some
advantages for instance superficial responses, poor
validation, and generation of vast quantities of data through
open ended items which may be problematic to analyze
[34]. These issues were addressed through addressing
concerns such as appropriate questionnaire design and
layout, clarity of instructions and language, length of
questionnaire, and proper induction of the participants to
the research study [35].
2.2. Venue and Sample
The research study was undertaken in Buret District,
Kericho County, Rift Valley Province of the Republic of
Kenya. One school was a Public Boy‘s Boarding School
while the other school was a Public Girl‘s Boarding School.
The total number of public schools offering computer
studies as an examinable subject was obtained from the
county education office as such schools would have their
computer laboratories sufficiently equipped and functional
to meet Government regulations [36]. This population was
separated to form two groups, one for Girls’ Schools and
one for Boys’ Schools. One school was then randomly
picked from each group to give the Girls’ School and the
Boys’ School which participated in the study. The
researcher obtained the proper research permit from the
government before proceeding to the schools of interest to
conduct the study. The researcher visited the schools and
after due introduction and permission from the school
administration, installed chatbot Knowie in the computers
in the computer laboratories of the two schools.
Discussion with the school administration concerning
classes to be involved in the study led to the identification
of Form Two as the level agreed upon to participate in the
study. Considerations for settling upon the Form Two
classes included issues such as the Form one students still
have to be taught more in the handling of computer
equipment, and upper forms (Form Three and Form Four)
being prepared for externally administered examinations.
Thus the Form level was determined purposively. Similar
considerations were made in settling upon Physics and
Computer Studies teachers as the study participant teachers.
All the teachers teaching Form Two were however trained
on chatbot use in the two schools to give the total number
of ten teachers in the two schools for eventual attitude data
collection and measurement.
2.3. Data Collection Instruments
The characteristic, or construct, that was measured for
the teachers was their attitude towards chatbot use in
teaching. Jain [37] states that attitude represents the
positive or negative mental and neural readiness towards a
person, place, thing or event and has a cognitive, affective
and behavioral component. Such an indirect construct is
commonly measured using items that are developed to
assess the construct. The score attained by a respondent to
the items designed to assess the construct will usually be
taken as a measure of the construct of interest. The rating
scale commonly used for measuring attitude is the Likert
scale. The basis of the Likert scale is the notion that
attitudes vary along a dimension from negative to positive,
and ‘the key to successful attitude measurement was to
convey this underlying dimension to survey respondents,
so that they could then choose the response option that best
reflects their position on that dimension’ [38, p2]. The
implication of this is that of universal applicability,
allowed variations in item wording as long as the
negative-to-positive dimension is covered, and assignment
of a common numerical code to gauge respondents view on
a particular item and across all the items. Hence with
multiple items in an attitude survey, the codes can be
summed or averaged to give an indication of each
respondent’s overall positive or negative orientation
towards that object [38].
A Likert Scale questionnaire containing twenty items
was used to establish teacher attitude towards use of
chatbots in teaching-learning. The attitude measurement
items in the questionnaire were designed to elicit teacher
attitude using a 5-point Likert scale (e.g. Chatbot use in
learning helps to clarify topic content; 1 – Strongly
Disagree, 2 – Disagree, 3 - Uncertain, 4 – Agree, 5 –
Strongly Agree). The items were arrived at after due
consideration of similar items that have been used in
teacher attitude studies relevant to computer use for
teaching and learning by teachers in schools [39-42]. The
number of such items in the questionnaire was eighteen.
Teacher attitude towards chatbot use in their instruction
was represented as an average score on the five-point scale,
with a score above 3.00 taken as indicating positive attitude.
Following these items was an item seeking to know
whether or not the teacher was willing to chat again with
the chatbot. Lastly, an open-ended item after the above
nineteen items sought to elicit suggestions from participant
teachers on how a chatbot could be further improved to
better meet their teaching needs.
2.4. Piloting
A piloting school similar to the ones that were ultimately
involved in the final study was earlier before study
commencement purposively identified and the chatbot
installed in the computer lab of that school. One Form Two
class stream was then picked for involvement during the
first piloting phase. Teachers teaching the class were
trained on chatbot use, after which students were also
trained with the assistance of the teachers that had been
trained. The participant teachers and students received this

1590 Teacher Attitude towards Use of Chatbots in Routine Teaching
initial training in the first two weeks of the term. Following
this, the teachers and students used the chatbot for
teaching-learning activities once a week for three weeks in
first term of the school year (third week to fifth week), after
which the teacher questionnaire was administered in two
occasions a fortnight apart. This test-retest procedure
provided data that was used to determine the reliability
coefficient of the attitude questionnaire. Results were used
to adjust the instrument accordingly, after which the final
instrument was administered as outlined above using a
second school. The results of this second piloting phase
were used to establish the reliability coefficient of the final
attitude questionnaire used in the study. The test-retest
reliability coefficient for the teacher attitude towards
chatbot use for their teaching instrument was 0.761, which
was adequate for the research purposes.
2.5. Data Collection Procedures
The total number of public schools offering computer
studies as an examinable subject was obtained from the
county education office. This was separated to form two
groups, one for Girl’s Schools and one for Boy’s Schools.
One school was then randomly picked from each group to
give the Girl’s School and the Boy’s School which
participated in the study. The researcher then visited the
schools and after due introduction and permission from the
school administration, installed the chatbot in the
computers in the computer laboratories of the two schools.
Discussion with the school administration considering
classes to be involved in the study led to the identification
of Form Two as the level agreed upon. Consideration
included issues like the Form one students still having to be
taught more in handling computer equipment, and upper
forms being final internal and National examination
preparation oriented. Thus the Form level was determined
purposively. Similar considerations were made in settling
upon Physics and Computer Studies teachers as the study
participant teachers. The teachers who used the chatbot in
actual teaching-learning activities were therefore two.
However, all the teachers teaching Form Two were trained
on chatbot use in the two schools to give the total number
of teachers as ten for attitude measurement. The study was
conducted for ten weeks in term one and ten weeks in term
two to give a total duration of twenty weeks during which
observations were conducted and teachers were exposed to
use of chatbots for teaching-learning purposes. In order to
determine the attitude that participant teachers had towards
chatbot technology use in their teaching and to elicit their
suggestions on how further the technology could be
improved to better suit their needs, the teachers completed
the questionnaire with relevant attitude-eliciting items after
their experience of chatbot use in their teaching.
2.6. Data Analysis Procedures
The overall average rating for all attitude measurement
items for the teachers who participated in the study was
determined from the tallied responses per questionnaire
item. Analysis of individual teacher responses to each
questionnaire item was also carried out to ascertain
majority teacher views per item and to note any
peculiarities of responses. Teacher responses to the last
open-ended item seeking chatbot improvement suggestions
from them were analyzed, categorized, frequency tallied
and sorted in descending order to indicate overall priority
for solution.
2.7. Results and Discussion
The percentage responses to each of the items in the
teacher questionnaire given by the respondents are
presented in the following charts.
Teacher responses to each of the items in the
questionnaire were as follows:
Item 1. Expand chatbot use to all subject topics
All the teachers agreed that chatbot use be expanded to
all topics in their teaching subject.
Item 2. Chatbot use is interesting
A majority of the teachers agreed that chatbot use is
interesting.

Universal Journal of Educational Research 6(7): 1586-1597, 2018 1591
Item 3. Chatbot use clarifies topic content
A majority of the teachers agreed that chatbot use
clarifies topic content.
Item 4. Chatbot use be undertaken by all students
A majority of the teachers agreed that chatbot use be
undertaken by all students.
Item 5. Expand Chatbot use to other school subjects
The majority of teachers agreed that chatbot use be
expanded to other school subjects.
Item 6. Use chatbot during regular lesson time
Teachers have reservations about using a chatbot during
regular lesson time.
Item 7. Chatbots are not hard to use
A majority of teachers agreed that chatbots are not hard
to use.
Item 8. Am not good in using chatbots for teaching
The majority of teachers disagreed that they were not
good in using chatbots for teaching.

1592 Teacher Attitude towards Use of Chatbots in Routine Teaching
Item 9. I like using chatbots for teaching
The majority of teachers agreed that they liked using
chatbots for teaching.
Item 10. Teaching a chatbot is not hard
A majority of the teachers agreed that teaching a chatbot
is not hard.
Item 11. Chatbot use is a waste of time
A majority of the teachers did not think that chatbot use
is a waste of time.
Item 12. It is enjoyable working with a chatbot
A majority of the teachers agreed that it was enjoyable
working with the chatbot.
Item 13. Difficulty experienced working with the chatbot
A majority of the teachers experienced little difficulty
working with the chatbot.
Item 14. Better topic teaching with a chatbot
A majority of the teachers agreed that there is better
topic teaching with the chatbot.

Universal Journal of Educational Research 6(7): 1586-1597, 2018 1593
Item 15. Extend chatbot response beyond school subjects
A majority of teachers felt that chatbot responses should
be extended beyond school subjects.
Item 16. I am confident teaching with a chatbot
The teachers were confident teaching with the chatbot.
Item 17. Chatbot use improves student understanding of subject content
A majority of the teachers felt that chatbot use improves
student understanding.
Item 18. Chatbots are friendly and helpful
A majority of teachers found the chatbot friendly and
helpful.

1594 Teacher Attitude towards Use of Chatbots in Routine Teaching
The individual and overall attitude scores for attitude questionnaire items for the teachers who responded to the
questionnaires in the two participant schools are presented in Table 2 below.
Table 2. Teacher Attitude towards Chatbot Use in Instruction Score Summary
N Min Max Mean Std. Deviation
Expand Chatbot Use To All Subject Topics 10 4.00 5.00 4.4000 .51640
Chatbot Use Interesting 10 2.00 5.00 4.3000 1.05935
Chatbot Use Clarifies Topic Content 10 2.00 5.00 4.0000 .94281
Chatbot Use Be Undertaken By All Students 10 1.00 5.00 3.7000 1.41814
Expand Chatbot Use To Other School Subjects 10 2.00 5.00 4.2000 .91894
Use Chatbot During Regular Lesson Time 10 1.00 5.00 3.1000 1.19722
Chatbots Not Hard To Use 10 3.00 5.00 4.1000 .56765
Am Not Good In Using Chatbots For Teaching 10 1.00 5.00 2.6000 1.57762
Like Using Chatbots For Teaching 10 1.00 5.00 3.7000 1.33749
Chatbot Teaching Not Hard 10 1.00 5.00 3.7000 1.25167
Chatbot Use Waste Of Time 10 1.00 5.00 2.0000 1.24722
Enjoyable Working With Chatbot 10 1.00 4.00 3.4000 .96609
Difficulty Experienced Working With Chatbot 10 1.00 4.00 2.0000 1.15470
Better Topic Teaching With Chatbot 10 1.00 4.00 3.3000 1.15950
Extend Chatbot Response Beyond School Subjects 10 1.00 5.00 4.1000 1.28668
Confident Teaching With Chatbot 10 2.00 5.00 4.1000 1.19722
Chatbot Use Improves Student Understanding Of Subject Content 10 1.00 5.00 3.9000 1.19722
Chatbots Friendly And Helpful 10 1.00 5.00 3.7000 1.05935
Valid N (list wise) 10
Average attitude rating score 3.572
Source: Researcher’s Field Data
The average attitude rating score obtained of 3.572
indicates that teachers have a positive attitude towards the
use of chatbots in instruction. The interpretation and
implication of this is that teachers are generally positively
disposed to using chatbots in their teaching, and hence
would welcome the technology and put it to use in their
teaching.
The last item in the teacher attitude questionnaire was an
open-ended item seeking to elicit suggestions from the
teachers on how chatbot technology could be improved to
better suit their needs. Their suggestions in descending
order of frequency of mention are summarized in the chart
1. The top three improvement suggestions by teachers are
that chatbot should come with question and answers
already programmed to avoid time wastage, chatbot should
come with question and answers already programmed to
avoid student wrong answers, and chatbot to incorporate
search capability comparable to web searches.
Chart 1. Teacher chatbot technology improvement suggestions

Universal Journal of Educational Research 6(7): 1586-1597, 2018 1595
Key:
Improvement Improvement Suggestion
1
Chatbot should come with questions and answers
already programmed to avoid time wastage
2
Chatbot should come with question and answers
already programmed to avoid student wrong
answers
3
Chatbot to incorporate search capability
comparable to web searches
4 Greater teacher training on chatbot use
5 Extend to other students
6 Extend to other schools
7 More computers
8
Extend chatbot use to other school teaching
subjects
The response to the item that asked teachers whether or
not they were willing to chat again with the chatbot was
that the majority (90%) were willing to chat again with the
chatbot.
Item 19. Willingness to chat again with the chatbot
3. Conclusions and Recommendation
Teachers regard technology as being of benefit to them
and to students [13, 43 - 45] and this regard extends to
chatbot use in teaching and learning. Teacher high regard
for chatbot technology use in teaching should therefore be
capitalized upon to enable them start transforming
teaching-learning environments from being the staple one
of conventional approach to that based on
social-constructivist ideas and thereby attain better
teaching and learning outcomes. This requires that teachers
be trained on proper educational technology integration
strategies, for as has been noted in literature, ‘investment in
new ways of learning and teaching is not the same as
investment in technology and infrastructure’ [46, p23],
with technology still predominantly being just availed to
schools and little concerted effort being expended on
teacher pedagogical views and actual school ICT
integration issues [47].
Teachers are ambivalent when it comes to the question
of when a chatbot should be used for teaching. The
underlying motivation as pointed out by the teachers is that
of time wastage in the face of need for syllabus coverage,
packed school timetable, and preparation for nationally
administered final exams. Ways therefore ought to be
found to avail time in schools for adequate technology use
in teaching through for example avoiding curriculum
overload [48-49]. Teachers also expressed a requirement
for a chatbot to be able to respond to topics outside school
teaching-learning subject matter. Chatbots meant for use in
teaching-learning, through proper knowledge base design,
can therefore be enabled to serve as a means for teachers
and students to access information beyond immediate
school learning content and affairs.
Concerning the top three improvement suggestions by
teachers (chatbot should come with question and answers
already programmed to avoid time wastage, chatbot should
come with question and answers already programmed to
avoid student wrong answers, and chatbot to incorporate
search capability comparable to web searches), a balance
ought to be struck between providing no content, some
content, and full content pertaining to teaching-learning
topics in educational chatbots. Some content is ideal, to
cater for time constraints and wrong student answers. Full
content is not ideal, as this would take away the element of
social knowledge construction by students and teachers
when using a chatbot for teaching-learning activities within
a social-constructivist based teaching-learning
environment. Search features to enable the chatbot to
undertake internet searches is important, as this would
improve the learning curve of a given chatbot rapidly and
thereby extend the comprehensiveness of its responses to
teacher and student questions.
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