Chatbot-mediated Learning: Conceptual Framework for the Design
of Chatbot Use Cases in Education
Stefan Sonderegger and Sabine Seufert
Institute for Educational Management and Technologies, University of St.Gallen,
St. Jakob-Strasse 21, 9000 St.Gallen, Switzerland
Keywords: Chatbots in Education, Chatbot-mediated Learning, Conversational Learning, Pedagogical Conversational
Agents, Tutorial Dialogue.
Abstract: While chatbots or conversational agents are already common in many business areas, e.g. for customer support,
their use in the education sector is still in its infancy. Chatbots might take over the role of a teacher, tutor,
conversational partner, learning analyst, team member, support assistant, or recommender system. Within
these different roles, chatbots can enhance learning and inherently address many requirements and success
factors for learning. The scalability and adaptiveness of conversational AI allow an individualised learning
support for all learners combined with collaboration opportunities and thus more equality in education. In this
context, the paper at hand discusses this pedagogical potential of chatbots in different roles and social settings
resulting in a conceptual framework for the understanding and design of chatbot use cases in education. Based
on success factors for learning derived from established learning theories and reports, core attributes and goals
of chatbot learning are deducted within three pedagogical domains of individual, social and analytic chatbot
learning. By combining this pedagogical dimension with a technological and content dimension, the presented
conceptual framework provides an overview of possibilities of how chatbots in education can be used and
The educational use of chatbots chatbot-mediated
learning – is an emerging research field in education.
Chatbots are artificial intelligence (AI) based
programs that aim to simulate human conversation
(Garcia Brustenga et al., 2018). It can be assumed that
such conversational agents are also suitable for
certain tasks in the field of education and learning. To
date, however, chatbots are not yet widely used in
education. The aim of this research paper is set in this
context. To advance the pedagogical implementation
of chatbots in education, it is important to find out
what has already been done, to structure this
knowledge and make it understandable for
pedagogical practice. This specific research stream is
interdisciplinary and addressed by researchers from
fields like computer science, education, linguistics,
psychology, and business informatics. This leads to
complementary but different research procedures and
evaluation approaches (Hobert, 2019).
From an educational perspective it seems
essential to further identify what pedagogical uses
and capabilities a chatbot has in an educational
context. In this circumstance, it is also relevant to
discuss the different roles and settings in which a
chatbot can be useful (e.g individual or team learning
situations) in relation to success factors for learning.
In addition, for the design of chatbot use cases, it is
fundamental to consider the technological maturity
and integration into educational systems to enhance
the chatbot's capabilities.
Research in this direction seems sensible for
several reasons. On the one hand, there are promising
pedagogical possibilities and on the other hand, one
can address future skills and competences. Visible
learning and the individual support of learners by
teachers or human tutors are somewhat neglected due
to large course sizes and an emerging number of
online learning scenarios. Both, learning theories and
empirical learning studies suggest the relevance of
learner-centred learning, individual support, a culture
of inquiry, continuous feedback and monitoring,
formative feedback, and so on (Bransford et al.,
2000), (Hattie & Yates, 2013). International
frameworks for 21st-century learning suggest that
critical thinking, making judgments and decisions,
clear communication, collaboration, and
Sonderegger, S. and Seufert, S.
Chatbot-mediated Learning: Conceptual Framework for the Design of Chatbot Use Cases in Education.
DOI: 10.5220/0010999200003182
In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 1, pages 207-215
ISBN: 978-989-758-562-3; ISSN: 2184-5026
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
technological awareness are crucial competencies in
the future (ISTE, 2017). Chatbots might support
learners to develop, improve and reflect these
competencies. Furthermore, students work hand in
hand with digital assistants, which becomes standard
in future work activities. The authors of the book
“Human + Machine. Reimagining Work in the Age of
AI” argue that humans need new skills to work with
smart machines and that we need a deeper
understanding of the complementary human-machine
interaction (Daugherty & Wilson, 2018): “Humans
are needed to develop, train, and manage various AI
applications. In doing so, they are enabling those
systems to function as true collaborative partners. For
their part, machines in the missing middle are helping
people to punch above their weight, providing them
with superhuman capabilities, such as the ability to
process and analyse copious amounts of data from
myriad sources in real-time. Machines are
augmenting human capabilities” (p. 6).
Within this paper, we focus on the pedagogical
foundations of human-machine interaction with
chatbots in education and address the following
research questions: RQ1. What pedagogical benefits
and capabilities do chatbots have in an educational
context? RQ2. How can a framework for the use of
chatbots in education be conceptualized? The goal is
to elaborate and communicate the potential of AI-
based chatbots to function as an individual or
collaborative learning partner and to augment
student’s capabilities. Based on the state of research
(section 2) we present a conceptual framework
aiming at providing a pedagogical basis for the
educational use of chatbots (section 3) before we
conclude with final remarks.
Based on the stated research goal, we identify
research on intelligent chatbots in general and
research on chatbots in education as relevant.
2.1 Research on Intelligent Chatbots
While the first chatbot named Eliza was already
developed over 50 years ago by Weizenbaum (1966),
the major developments have happened in recent
years. Core technologies or components of modern
chatbots like automated speech recognition (ASR),
natural language processing (NLP) and text-to-
speech engines rely on deep learning neural networks
and thus AI technologies. Due to technological
milestones and the increasing attention to AI,
chatbots, also known as conversational agents or
natural dialog systems, are an emerging field of
interest in many areas such as e-commerce, health,
finance, service industries, and education. From a
business perspective chatbots mainly allow to
improve customer service and to reduce service costs,
from a user’s perspective important motivations to
use chatbots are productivity, entertainment, social
factors and novelty interaction (Adamopoulou &
Moussiades, 2020).
Examples of modern and widely known chatbots
are Amazon’s Alexa, Apple’s Siri, Microsoft’s
Cortana or Google Assistant, which can also be
categorized as personal assistants mostly used for
customer service and information acquisition or as a
user interface for mobile devices (Cahn, 2017).
Adamopoulou and Moussiades (2020) offer a detailed
chatbot categorization and differentiate between
informative, conversational or task-based chatbots to
point out the main goal of the chatbot. A further basic
categorization of chatbots can be the knowledge
domain. Some chatbots have one or more specific
knowledge domains, whereas a generic chatbot is
designed to answer any user question. While we refer
to both categories as chatbots, other researchers with
a more technical perspective prefer the umbrella term
‘conversational systems’ and then differentiate
between more domain- or task-specific ‘dialog
systems’ and generic ‘chatbots’ (cf. Chen et al.,
2017). An example for a generic and state-of-the-art
chatbot that has won the Loebner Prize Turing test for
best chatbot several times in recent years is Mitsuku
( Services like IBM Watson or
Google Dialogflow offer platforms and frameworks
for companies or institutions to build and train
domain-specific chatbots based on transfer learning
techniques. The chatbot already knows how to learn
(pre-trained) and is fed with domain-specific
knowledge and rules for its specific use case. This
allows customization and personalization of the
chatbot based on a given basic structure. Most
chatbots are set on a retrieval-based approach, where
responses are generated based on pre-trained rules
and matched through machine learning classification
tasks. While such retrieval-based models promise
accurate and correct responses in case of a correct
match, they are unable to answer unseen questions or
intents without predefined responses or actions
(Winkler & Söllner, 2018). This problem can be
solved with generative models, the newest generation
chatbots. Generative models do not answer with pre-
defined answers but try to generate their answers
CSEDU 2022 - 14th International Conference on Computer Supported Education
based on the context, previous dialogs, and a
pretraining based on real dialogs (Cahn, 2017). The
amount of available dialog data is therefore a key
success factor for underlying deep learning models.
Only intensive training enables a chatbot to recognize
and adapt patterns in human dialogs based on
statistically frequent answers (Spierling &
Luderschmidt, 2018). Retrieval-based models are
considered as more reliable until today due to
simplicity, while generative models are better for text
generation and promise a more real conversation
(Molnar & Szuts, 2018). On the downside, the
generative model can only be as good as its
underlying training data. So, if the data is flawed,
corrupted or biased, so is the chatbot. This may be one
reason why the two approaches are increasingly
Considering this technical side and development
of chatbots we can draw on recent studies defining
technical dimensions to categorize educational
chatbots (Winkler & Söllner, 2018), (Molnar & Szuts,
1) Building approaches, where retrieval-based
models are distinguished from generative
models. While the former are based on a set of
predefined responses, using an algorithm to
select the best-matching response, the latter
generates responses based on the input.
2) Input mode of chatbots, in particular the
question of whether speech over text input
might be appropriate for our context and
learning design.
3) Inclusion of contextual information, such as for
example time, location, user information,
learning path data, in order to select the right
From an ultimate technological perspective, the
goal of a chatbot and consequently the evaluation
focus might be to pass the so-called Turing test (cf.
Turing, 1950), meaning that the optimal chatbot
cannot be distinguished from humans. Cahn (2017)
mentions further perspectives to evaluate
performance: From a user experience perspective
another goal would be user satisfaction, from a
linguistic perspective a goal would be for the chatbot
to speak grammatically correct and meaningful and
from an information retrieval perspective chatbots
should also be evaluated according to the specific
function (Cahn, 2017). In an educational context, this
function and therefore the evaluation perspective
might differ again from case to case but would
additionally include learning outcome, learning
success, and learner motivation.
2.2 Chatbots in Education
In recent years the spread of chatbots and the research
on chatbot development, design, and use has
increased and advanced. Følstad et al. (2020) are
convinced that chatbots are maturing for application
areas including education and may be designed for
individual users or for supporting collaboration.
Previous research on chatbots in education often
focuses on designing messenger-like chatbots but
there might be a lack on generalizable results (Meyer
von Wolff et al., 2020). Winkler and Söllner (2018)
conducted an extensive literature review and
conclude that “the effectiveness of chatbots in
education depends on individual student differences,
the ways of building chatbots, and the chatbot
mediated learning process quality” (p.29). While the
authors consider only few studies that suggest the
potential of chatbots for learning purposes so far, they
also emphasize the great potential of chatbots to
create individual learning experiences for students
and to support teachers. The exploration of this
potential in the field of technology-mediated learning
chatbot-mediated learning is a growing and
interdisciplinary research field. It can however, draw
on a rich body of previous research in different
educational research fields around pedagogical
agents and tutorial dialogue systems. Research in this
field suggests that both, support by a tutorial dialogue
agent and collaborative learning support lead to better
learning outcomes than supportless learning (Kumar
et al., 2007).
Compared to traditional intelligent tutoring
systems or pedagogical agents in e-learning
scenarios, chatbots do not only give instructions or
provide feedback, but can also react to individual
intents and create a real personalization and more
importantly, a learner-centred approach (Winkler &
Söllner, 2018). While these technologies can be
integrated and build on each other, chatbots can be
regarded as conversation technologies that have a
more stand-alone character compared to adaptive
learning systems. Chatbots in education can still have
different user interfaces or be embedded in other
systems like a Learning Management System (LMS).
The main difference between chatbots in education
compared to other contexts is probably the integration
or self-storage of learning objects or even learning
paths (Hobert, 2019). Figure 1 illustrates a technical
setup of a chatbot in an educational setting in a high-
level abstraction.
With the ultimate goal to enhance and enable a
learner-centred individual and collaborative learning
setting, chatbots promise to have a positive impact on
Chatbot-mediated Learning: Conceptual Framework for the Design of Chatbot Use Cases in Education
Figure 1: Educational Chatbot Model. (cf. Seufert et al., 2021).
student motivation, satisfaction, and learning success
(Winkler & Söllner, 2018). In education practice,
however, the productive use of chatbots is still in its
infancy. Nevertheless, research groups see great
potential of chatbots in education and present
promising use cases for different tasks such as
learning assessments, reflections, language learning,
motivating, mentoring, administration, or
productivity assistant (Garcia Brustenga et al., 2018).
Most studies, that have shown successful
implementations of chatbot learning scenarios (cf.
Dutta, 2017; Goel et al., 2016; Huang et al., 2017),
are based on projects with isolated chatbot tasks, e.g.
to answer frequently asked questions, to handle forum
posts, or to ask questions in language learning
applications. Language learning is a more advanced
application of chatbots in education. Fryer et al.
(2019) summarize that in early research chatbots as
language practice tools were shown to be useful for
advanced and motivated students, but showed
limitations in terms of in- and output quality. More
recent research shows, that the linguistic quality has
improved significantly and that chatbot conversations
are carried on longer but with fewer words and
vocabulary within messages compared to human-
human conversations (Fryer et al., 2019).
Another example of more advanced chatbot use
cases in education is supporting students with course
and administrative information or offering screening
tests via chatbot. This application is already
implemented at universities worldwide but can, at
least until today, be considered more of a customer
service chatbot use case than chatbot-mediated
learning. Within a comprehensive conceptual
framework, such use cases could in a further sense be
assigned to educational recommender systems. These
are seen as electronic systems containing domain
knowledge, learner information, and knowledge of
the teaching strategies which seek to improve
learning (Bodily & Verbert, 2017). A future chatbot
might also combine and integrate the intelligence of
the different student-facing learning analytics
systems distinguished by Bodily and Verbert (2017):
Learning Analytics Dashboards, Educational
Recommender Systems, Educational Data Mining
Systems, Intelligent Tutoring Systems. Another
related approach addressing the integration of AI
technologies in education is cognitive computing in
education, where a cognitive assistant (e.g. a
cognitive bot) would combine different AI services
(Lytras et al., 2019).
As introduced in section 2.1, there is still the
question of how to evaluate educational chatbots in
terms of learning outcomes, learner motivation,
learner satisfaction or other constructs. Winkler and
Söllner (2018) propose a learning taxonomy model
(Anderson, 2001) as a basis for the evaluation of
learning outcomes, further evaluating the influence of
chatbots on self-efficacy and self-regulation skills.
Within 25 studies reviewed in the field of chatbot-
mediated learning, Hobert (2019) identified 7
evaluation objectives (acceptance and adoption,
learning success, motivation, usability, technical
correctness, further psychological factors, and
beneficial effects) and matched the objectives with
the main research procedures identified (Wizard-of-
Oz experiment, technical validation, laboratory
experiment, field experiment). The author emphasizes
that most studies analyse only selected aspects and he
attributes this to the interdisciplinarity of this field
(Hobert, 2019). This fact supports the aim of this paper
to present a comprehensive framework and to provide
a basis for future research projects that evaluate the use
of chatbots beyond single evaluation objectives.
Speech to text
Interaction database
User and dialog history, data
and learning analytics
Sentiment analysis
Context database
Domain knowledge, intents
and entities
External data requests
LMS, Web APIs, external databases
Channels: Application, Webservice, Messenger, Social Media, LMS, AR, VR, Social Robot, …
Text to speech
Sentiment output
Input format:
- Audio
- Picture / video
- Haptic / various sensors
Output format:
- Picture / video
- Haptic / various actuators
CSEDU 2022 - 14th International Conference on Computer Supported Education
Within this section, we present a conceptual-level
framework to understand chatbot-mediated learning
and to design pedagogical chatbot use cases. The goal
of the presented framework is to lay out what
pedagogical uses and capabilities a chatbot has in an
educational context. Hence, the presented framework
can offer a foundation for the conceptual and
pedagogical design of chatbot use cases and to
uncover the further potential and limitations of the
underlying technologies within education.
Table 1: Chatbots in Education – Maturity Levels.
TPACK Level 1 Level 2 Level 3
chatbot learning
+ Social
+ Metacognition
and analytics
Simple rule-
based chatbot
+ Supervised
learning AI
+ Sentiment
+ Unsupervised
learning AI /
+ Omniscient
Note: Categories based on TPACK model (Koehler & Mishra,
The structure of the presented framework is based
on the Technological Pedagogical Content
Knowledge (TPACK) model, which is widely used in
educational research. The TPACK model originally
represents a framework for teacher knowledge for
technology integration Koehler and Mishra (2009).
Even though a chatbot should primarily be learner-
centred it also should integrate these same three and
closely connected components of educator
knowledge. Table 1 gives an overview of the three
components by which we study chatbots in education
and in a first step indicate high-level maturity levels.
The theoretical background in section 2 has
shown that present chatbots in education are mainly
designed for personal learning and as conversational
partners or tutors, with an information retrieval
approach based on domain knowledge (maturity level
1-2). Future chatbots might include more
collaborative learning, analytics (cf. Ifenthaler &
Schumacher, 2016) and metacognition functions, use
more advanced AI and generative models, and
additionally retrieve and process social and context
information (maturity level 2-3).
3.1 Pedagogical Perspective and Goals
With a focus on educational maturity, we intend to
substantiate the potential of chatbot learning with
learning theory in the form of connections with
established learning theories and more recent learning
reports and empirical studies (Table 2). A corpus of
learning-related theories (Bandura, 1997; Deci &
Ryan, 2012; Leventhal et al., 1984) and reports
(Bransford et al., 2000; Hattie & Yates, 2013; ISTE,
2017) was chosen based on broad acceptance in
educational research and related based on its key
concepts and constructs for learning success. Table 2
illustrates the pedagogical perspective and theoretical
deduction of core attributes and goals of chatbot
Table 2: Chatbots in Education – Pedagogical Perspective and Goals.
chatbot learning
chatbot learning
Metacognition &
Core attributes and goals
of chatbot learning
based on underlying
theories and re
Personalized and
ased learnin
Collaboration and
network memor
Learning progress and
formative assessment
Individual learning pace Social embedding Feedback & reflection
Self-determination theory
(Deci & Ryan, 2012)
Autonomy experience
Social relatedness Competence experience
Self-efficacy / social cognitive
Bandura, 1997
Self-regulation theory
Leventhal et al., 1984
Self-Reflection Team-Reflection Metacognition;
How to learn
ord et al., 2000
Learner centred;
considered learn
Culture of inquiry Assessment centred;
continuous monitorin
Framework 21st century learning
(ISTE, 2017)
Communication skills;
decision making
Collaboration skills;
creativity; empathy
Critical thinking;
Use of technology
Visible learning
Hattie & Yates, 2013
Reciprocal teaching
Formative evaluation;
nitive strate
Chatbot-mediated Learning: Conceptual Framework for the Design of Chatbot Use Cases in Education
Table 3: Conceptual Framework for Chatbots in Education.
Dimensions Characteristics (can be met supplementary)
Pedagogical dimensions
Chatbot role in
individual learning setting
Cf. (Garcia Brustenga et al.,
Support assistant
e.g. research assistant,
FAQ, Nerdybot
Conversational partner
e.g. communication
trainer, tutor
Recommender system
e.g. learning path
Learning analyst
e.g. formative
Chatbot role in
social learning context
Cf. (Garcia Brustenga et al.,
e.g. storytelling, debater,
presenter, teaching
Team member
e.g. maintain project
documentation, team
support, research
Collaboration enhancer
e.g. connect teams,
structure teamwork
Team analyst
e.g. analyze teamwork
and provide feedback
Learning Analytics
Cf. (Fryer et al., 2019;
Ifenthaler & Schumacher,
Summative assessment
(ask questions and give
Formative assessment
(reflect on learning
progress, continuous
Intelligent edu-
recommender system
(continuous learning
process monitoring)
Emotion analytics
(Monitor and analyse
sentiments /emotions
to improve learning)
Technological dimensions
Interaction Cf. (Spierling &
Luderschmidt, 2018)
Visual based
(e.g. text)
Audio based
(e.g. speech)
Virtual presence
(e.g. virtual agent)
Physical presence
(e.g. robot)
Cf. (Lytras et al., 2019;
Molnar & Szuts, 2018)
Simple rule-based model Retrieval-based model Generative models and
unsupervised learning
Social intelligence:
Sentiment analysis,
emotion detection
Embedding / Channel Local application (mobile
/ computer)
(on any device)
Social Media
(known channel)
Embedded in LMS
(on any device)
Content dimensions
Contextual data integration
Cf. (Molnar & Szuts, 2018;
Winkler & Söllner, 2018)
Basic domain knowledge
Basic context data
(time, location, user)
Personalisation with
conversation history
Personalisation with
learner information
(learner path /grades)
Knowledge base
Cf. (Anderson, 2001)
Factual domain
Conceptual and
procedural knowledge
Metacognitive and
social knowledge
learning. The bullet point attributes and goals
included, describe elementary principles and success
factors for learning and create a basis for linking
chatbot learning with concrete didactic goals. They
thus help in the justification of chatbot learning and
as a conceptual basis for concrete goals and their
3.2 Conceptual Design of Chatbot Use
Cases in Education
At the beginning of a project to design and use a
chatbot in education, the following questions are of
central importance in connection with the objective:
What are the (pedagogical) goals of the chatbot
use? What is the context?
Which target group does the chatbot address?
What is the role and what are the tasks of the
chatbot? What is the role of the learner?
What are the limitations or technological
requirements? What (sensitive) data is used?
What is the time, personnel and financial
Since chatbot projects in education, just like
innovations in learning technologies, are often driven
by a technological direction, it is recommended to
combine the pedagogical with the technological
perspective at an early stage to develop a shared new
vision of learning (Dillenbourg, 2016). Based on the
pedagogical perspective from section 3.1 one can
identify the educational setting and define desired
learning conditions and pedagogical goals in order to
answer the questions posed.
Accordingly, in table 3 we lay out the core of the
conceptual framework for the conceptual design of
chatbot use cases in education, based on the
pedagogical perspective from section 3.1 and the
theoretical and technical background from section 2.
The three components of the TPACK model serve as
the main structure. The goal is a collectively
configuration of the pedagogy, technology and
content dimensions. Each dimension has four
CSEDU 2022 - 14th International Conference on Computer Supported Education
characteristics that can be met supplementary (e.g. a
chatbot can have the role of a conversational partner
and a learning analyst combined, or be available over
multiple channels).
When planning a chatbot use case in education,
the pedagogical point of view is central together with
the goal of the chatbot use. The role of the chatbot in
an individual or social learning setting already partly
determines the further requirements. At the same
time, the roles or tasks that a chatbot can take on in
practice today are often limited by the technological
possibilities and boundaries. An extensive overview
as well as practical examples of chatbots in education
divided by tasks is provided by Garcia Brustenga et
al. (2018). When it comes to developing a chatbot in
education, Satow (2019) describes the following
development steps:
Creating the bot concept
Analysis of real dialogs and questions
Creation of bot scripts
Bot training by defining intents
Bot skills development
Testing the chatbot
Optimize in productive use
When designing chatbot dialogs, general and
education specific design principles can be helpful
(cf. Yu et al., 2016). Cahn (2017) describes 'human
imitation strategies' that have proven successful (e.g.
personality development, conversation control,
human errors). And in terms of developing a chatbot
for learning purposes, Smutny and Schreiberova
(2020) offer a list of attributes describing the quality
of an educational chatbot within the categories
teaching (e.g. set goals and monitor learning
progress), humanity (e.g. able to maintain themed
discussion), affect (e.g. entertaining, engaging) and
accessibility (e.g. responds to social cues
From a technological point of view, in addition to
the form of interaction and chatbot intelligence, the
main question is its integration or embedding.
Educational institutions such as schools and
universities as well as educational organisations in
companies often use learning management systems.
Here it is important to clarify whether the chatbot can
be integrated into existing learning platforms or, if
other channels are used, how the learner authenticates
himself to the chatbot, if necessary. This is especially
important if the chatbot is to access not only general
knowledge (factual, conceptual, procedural or social)
but also contextual knowledge about the individual
learner from a content perspective. This can be,
among other things, personal, performance-related or
behavioural data. The data basis and its use result in
limiting factors and requirements, which are
discussed in the following section 3.3.
3.3 Limitations
Depending on the role of the chatbot, data storage or
connection to databases, it is important to clarify the
topics of data protection, data storage, data security,
data integrity and data deletion at an early stage.
Since AI services are often computing power-
intensive and therefore cloud-based, the privacy of
the individual is all the more important (Walsh,
2018). According to (Cahn, 2017), chatbots via
messenger applications or services are problematic
from a privacy perspective, as services such as
Facebook Messenger do not offer end-to-end
encryption by default and cannot guarantee user
identification. At the same time, many chatbots and
services include and process sensitive data (personal
data, images, audio, video). Services such as Amazon
Echo store recordings in the cloud, while many other
services send the data for further processing
unencrypted via APIs. All these factors speak in
favour of in-house development and local data
storage and processing. Nevertheless, researchers
also emphasise advantages of smart assistants or
messaging applications such as a familiar user
interface, no installation, no costs, integration of
games, sharing of media (Smutny & Schreiberova,
2020). Regardless of the technology chosen, there are
ethical issues to discuss. In addition to data
protection, there is an important demand for
explainable AI (XAI), which is particularly important
in the field of education (Gunning, 2017). Zanzotto
(2019) calls for responsible AI with a "human-in-the-
loop" and a clear knowledge life cycle to prevent a
bias of the AI or the chatbot.
Chatbots and conversational AI have the potential to
go beyond the pure simulation or imitation of human
interaction as defined in the introduction. They can
enhance human beings and learners in many possible
ways, individually or in groups, within a classroom or
outside, in business, in education, and daily life. The
human-machine interaction with chatbots in
education promises a variety of pedagogical
advantages and possibilities. Chatbots enable
personalized learner-centred and needs-based
learning, a main success factor for learning, for
student motivation and contribution according to
Chatbot-mediated Learning: Conceptual Framework for the Design of Chatbot Use Cases in Education
learning theory and studies. In a collaborative role,
chatbots promise to improve and enhance
collaboration skills, social embedding and team-
reflection. And based on learning analytics and access
to context knowledge including learning paths,
chatbots may make learning visible, improve
metacognitive strategies and foster learner’s
confidence and self-reflection through continuous
monitoring and feedback.
With the presented conceptual framework, we
provide an overview of possibilities how chatbots in
education can be used. The framework might help to
conceptualize a chatbot use case and underlying
pedagogical goals based on a configuration of the
presented dimensions covering the pedagogical,
technological, and content perspectives of an
educational chatbot. Besides the highlighted
pedagogical potential of chatbots in education, we
want to point out limitations regarding our framework
as well as chatbots in education in general. While the
framework presents a high-level understanding and
idea of the configuration, it does not address the
implementation process and its various obstacles that
require utmost attention, e.g. data privacy and
protection, data life cycle, copyrights, integration
issues on institution level, biases, information quality,
dependence on big technology suppliers, ethical and
legal questions and so on.
Future research could consider and focus on these
factors and the implementation phase of concrete use
cases while building upon the conceptual framework
and its underlying concepts and learning theories.
From a pedagogical and interdisciplinary perspective,
it would be interesting to work towards a more
comprehensive evaluation of various success factors
and basic conditions for learning, in addition to
specific evaluations of chatbots in terms of individual
measurable target variables.
Adamopoulou, E., & Moussiades, L. (2020). Chatbots:
History, technology, and applications. Machine
Learning with Applications, 2(53), 100006.
Anderson, L. W. (Ed.). (2001). Pearson education. A
taxonomy for learning, teaching, and assessing: A
revision of Bloom's Taxonomy of educational objectives
(Abridged ed.). Longman.
Bandura, A. (1997). Self-efficacy: The exercise of control.
Bodily, R., & Verbert, K. (2017). Review of Research on
Student-Facing Learning Analytics Dashboards and
Educational Recommender Systems. IEEE
Transactions on Learning Technologies, 10(4), 405–
Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000).
How people learn: Brain, mind, experience, and school.
Expanded edition. National Academy Press.
Cahn, J. (2017). CHATBOT: Architecture, Design, &
Chen, H., Liu, X., Yin, D., & Tang, J. (2017). A survey on
dialogue systems: Recent advances and new frontiers.
Acm Sigkdd Explorations Newsletter, 19(2), 25–35.
Daugherty, P. R., & Wilson, H. J. (2018). Human +
machine: Reimagining work in the age of AI.
Deci, E. L., & Ryan, R. M. (2012). Self-Determination
Theory. In P. van Lange, A. Kruglanski, & E. Higgins
(Eds.), Handbook of Theories of Social Psychology:
Volume 1 (pp. 416–437). Sage Publications.
Dillenbourg, P. (2016). The Evolution of Research on
Digital Education. International Journal of Artificial
Intelligence in Education, 26(2), 544–560.
Dutta, D. (2017). Developing an Intelligent Chat-bot Tool
to Assist High School Students for Learning General
Knowledge Subjects. Georgia Institute of Technology.
Følstad, A., Araujo, T., & Papadopoulos, S. (2020).
Chatbot Research and Design: Third International
Workshop, CONVERSATIONS 2019, Amsterdam, The
Netherlands, November 19–20, 2019, Revised Selected
Papers (1st ed. 2020). Information Systems and
Applications, incl. Internet/Web, and HCI.
Fryer, L. K., Nakao, K., & Thompson, A. (2019). Chatbot
learning partners: Connecting learning experiences,
interest and competence. Computers in Human
Behavior, 93, 279–289.
Garcia Brustenga, G., Fuertes Alpiste, M., & Molas
Castells, N. (2018). Briefing Paper: Chatbots in
Education. Universitat Oberta de Catalunya (UOC).
Goel, A., Anderson, T., Belknap, J., Creeden, B., Hancock,
W., Kumble, M., & Wilden, B. (2016). Using Watson
for Constructing Cognitive Assistants. Advances in
Cognitive Systems, 4.
Gunning, D. (2017). Explainable artificial intelligence
(xai). Defense Advanced Research Projects Agency
(DARPA), Nd Web, 2, 2.
Hattie, J., & Yates, G. C. R. (2013). Visible Learning and
the Science of How We Learn. Routledge.
Hobert, S. (2019). How Are You, Chatbot? Evaluating
Chatbots in Educational Settings – Results of a
Literature Review. 10.18420/DELFI2019_289
Huang, J., Lee, K., Kwon, O., & Kim, Y. (2017). A chatbot
for a dialoguq-based second language learning system.
CALL in a Climate of Change: Adapting to Turbulent
Global Conditions, 151.
Ifenthaler, D., & Schumacher, C. (2016). Learning
Analytics im Hochschulkontext. WiSt -
CSEDU 2022 - 14th International Conference on Computer Supported Education
Wirtschaftswissenschaftliches Studium, 45(4), 176–
ISTE. (2017). ISTE standards for students.
Koehler, M., & Mishra, P. (2009). What Is Technological
Pedagogical Content Knowledge? Contemporary
Issues in Technology and Teacher Education, 9, 60–70.
Kumar, R., Rosé, C. P., Wang, Y.-C., Joshi, M., &
Robinson, A. (2007). Tutorial dialogue as adaptive
collaborative learning support. Frontiers in Artificial
Intelligence and Applications, 158, 383.
Leventhal, H., Nerenz, D. R., & Steele, D. F. (1984). Illness
representations and coping with health threats. A
Handbook of Psychology and Health:
Sociopsychological Aspects of Health, 219–252.
Lytras, M., Visvizi, A., Damiani, E., & Mathkour, H.
(2019). The cognitive computing turn in education:
Prospects and application. Computers in Human
Behavior, 92, 446–449.
Meyer von Wolff, R., Nörtemann, J., Hobert, S., &
Schumann, M. (2020). Chatbots for the Information
Acquisition at Universities A Student’s View on the
Application Area. Chatbot Research and Design - 3rd
International Workshop, 2019, 11970 LNCS.
Molnar, G., & Szuts, Z. (2018). The Role of Chatbots in
Formal Education. In IEEE 16th International
Symposium on Intelligent Systems and Informatics
(SISY) (pp. 197–202). IEEE.
Satow, L. (2019). Lernen mit Chatbots und digitalen
Assistenten. In A. Hohenstein & K. Wilbers (Eds.),
Handbuch E-Learning. Wolters Kluwer.
Seufert, S., Guggemos, J., & Sonderegger, S. (2021).
Soziale Roboter im Bildungsbereich. In O. Bendel
(Ed.), Soziale Roboter (Vol. 22, pp. 475–494). Springer
Fachmedien Wiesbaden.
Smutny, P., & Schreiberova, P. (2020). Chatbots for
learning: A review of educational chatbots for the
Facebook Messenger. Computers & Education, 151,
Spierling, U., & Luderschmidt, J. (2018). Chatbots und
mediengestützte Konversation. In C. Kochhan & A.
Moutchnik (Eds.), Media Management (pp. 387–408).
Springer Fachmedien Wiesbaden.
Turing, A. M. (1950). Computing Machinery and
Intelligence. Mind, LIX(236), 433–460.
Walsh, T. (2018). It’s alive: Wie künstliche Intelligenz
unser Leben verändern wird. Edition Körber.
Weizenbaum, J. (1966). ELIZA: A computer program for
the study of natural language communication between
man and machine. Communications of the ACM
9(1966),1."Computational Linguistics" S. 36-45.
Winkler, R., & Söllner, M. (2018). Unleashing the
Potential of Chatbots in Education: A State-Of-The-Art
Analysis. 10.5465/AMBPP.2018.15903abstract
Yu, Z., Xu, Z., Black, A., & Rudnicky, A. (2016). Strategy
and Policy Learning for Non-Task-Oriented
Conversational Systems. Proceedings of the 17th
Annual Meeting of the Special Interest Group on
Discourse and Dialogue.
Zanzotto, F. M. (2019). Human-in-the-loop Artificial
Intelligence. Journal of Artificial Intelligence
Research, 64, 243–252.
Chatbot-mediated Learning: Conceptual Framework for the Design of Chatbot Use Cases in Education