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How to Plan and Manage a Blended Learning Course Module Using Generative Artificial Intelligence?

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Artificial intelligence (AI) is rapidly transforming the educational landscape, playing a crucial role in the transition to blended learning environments. As generative AI gains momentum, educators now have access to a growing repository of AI tools that can facilitate the shift from face-to-face instruction to more virtual learning experiences. This chapter provides a practical guideline for integrating and using AI tools to support educators in transitioning their courses to blended learning. The approach is structured around four key pillars: teacher practice support, online classroom support, evaluation and feedback, and student support. Following this guideline, we explore a curated list of AI-powered tools categorized based on their functions within these four pillars. To illustrate the application of these guidelines, we present a case study demonstrating a transition of a selected module of a traditional face-to-face machine learning course and make it accessible to students online, thus enabling blended learning experience. This chapter can empower future educators interested in AI to structure engaging blended learning courses and underscore the significant role of AI in enhancing the planning, management, implementation, and assessment of new blended learning courses.
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Chapter 4
How to Plan and Manage a Blended
Learning Course Module Using
Generative Artificial Intelligence?
Mohammad Khalil , Ronas Shakya , Qinyi Liu , and Martin Ebner
Abstract Artificial intelligence (AI) is rapidly transforming the educational land-
scape, playing a crucial role in the transition to blended learning environments. As
generative AI gains momentum, educators now have access to a growing repository
of AI tools that can facilitate the shift from face-to-face instruction to more virtual
learning experiences. This chapter provides a practical guideline for integrating and
using AI tools to support educators in transitioning their courses to blended learning.
The approach is structured around four key pillars: teacher practice support, online
classroom support, evaluation and feedback, and student support. Following this
guideline, we explore a curated list of AI-powered tools categorized based on their
functions within these four pillars. To illustrate the application of these guidelines, we
present a case study demonstrating a transition of a selected module of a traditional
face-to-face machine learning course and make it accessible to students online, thus
enabling blended learning experience. This chapter can empower future educators
interested in AI to structure engaging blended learning courses and underscore the
significant role of AI in enhancing the planning, management, implementation, and
assessment of new blended learning courses.
Keywords Generative AI in education ·Blended learning using AI ·AI guidelines
for blended learning ·Digital skills ·Teaching and learning in AIED
M. Khalil (B
)·R. Shakya ·Q. Liu
University of Bergen, Bergen, Norway
e-mail: mohammad.khalil@uib.no
R. Shakya
e-mail: ronas.shakya@uib.no
Q. Liu
e-mail: qinyi.liu@uib.no
M. Ebner
Graz University of Technology, Graz, Austria
e-mail: martin.ebner@tugraz.at
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
S. Panda et al. (eds.), Case Studies on Blended Learning in Higher Education,
https://doi.org/10.1007/978-981-97-9388-4_4
53
54 M. Khalil et al.
4.1 Introduction
Since the last pandemic, the imperative transitioning from face-to-face to online
teaching has become crucial for education, impacting not only the learning expe-
rience for students but also the planning and management of provided courses and
teaching practices (Ebner et al. 2020a). This transition holds significance across both
school and higher education settings. Even before the pandemic, integrating tech-
nology into education garnered considerable attention from institutions. However,
the complete adoption of online education during the pandemic has brought forth
numerous challenges and issues (Barrot et al. 2021; Mushtaha et al. 2022). For
instance, implementing a fully online pedagogical approach necessitates adaptation
from both management and teachers, while students experience increased pressure
on mental health and socialisation (Mushtaha et al. 2022).
Various academic studies and reports highlight that, at the moment, blended
learning may stand out as the most friendly instructional mode (Rasheed et al. 2020),
as well as its variants (Ebner and Schön 2019). This finding is supported by recent
survey results, where students expressed a preference for a blended model over solely
online or traditional learning (Mushtaha et al. 2022). Blended learning involves the
integration of face-to-face and online instruction, mediated by technology. Garrison
and Kanuka (2004) define it as “a thoughtful integration of classroom face-to-face
learning experiences with online experiences.” (p. 96) According to the authors, tran-
sitioning from face-to-face to blended learning necessitates particular design consid-
erations, such as the developmental level of a course, resource management, and
course structure planning. However, Garrison and Kanuka (2004) argue that blended
learning is both simple and complex. The simplicity lies in converting certain face-
to-face components to online components, while the complexity arises from the lack
of a definitive answer on how to do such a practice.
Blended learning comes in various model delivery forms, often combining face-
to-face classrooms, live sessions, and self-paced learning (Valiathan 2002). Ebner
et al. (2017,2020b) presented a summary of models for creating blended courses. The
first model, the traditional blended mode, starts with face-to-face meetings followed
by online sessions. The second model, inverse-blended learning, primarily empha-
sises online sessions with fewer face-to-face meetings, focusing on student-organised
group work. The third model involves flipped courses, where students access inter-
active components available online without live sessions and then participate in
face-to-face meetings, exams, and discussions with the teacher.
Though teachers may adopt different approaches to blended learning, there is a
common agreement to employ technology for converting face-to-face courses into
blended formats or integrating online components into traditional classes (Bayyat
et al. 2021). These models demand proper planning and management strategies to
help in this transition (Musdalifah et al. 2021). And this is quite challenging because
teachers are faced with additional challenges, especially they will need competencies
in different subjects—media design and educational technology, as well as further
digital skills. It is imaginable that transition leads to a higher workload, too.
4 How to Plan and Manage a Blended Learning Course Module Using 55
As an integral component of modern technology, Artificial Intelligence (AI) stands
as a key player in the field of education (Lo 2023; Jahic et al. 2023) which can be
helpful to transitioning to blended learning. As such, AI and automated content
creation can be set to change learning management systems for good, enabling
greater opportunities to customise courses, generate syllabuses, recommend course
structures, and adjust learning goals of courses (Darvishi et al. 2023). The body
of literature has begun to get richer with examples that employ AI applications to
help with online learning. For instance, prominent areas in recent works on AI in
education reported on utilising AI to help generate content for a variety of courses
(Khosravi et al. 2023), course and program curriculum mapping targeting faculties
and teachers, instant feedback for students (Afzaal et al. 2021), and synthetic data
generating to enable learning analytics improve future course designs (Liu et al.
2024) by extracting, gathering course relevant data, and visualizing and comparing
it. Yet, while the field of AI is experiencing rapid growth with an increasing number
of AI websites and tools emerging, the existing literature lacks guidelines and case
studies showcasing how to use it to help in improving online learning and support in
transforming into blended learning models.
In this chapter, we introduce our established guidelines for integrating and using
AI tools to guide teachers to transition part of their courses to blended learning.
We then provide our surveyed landscape of potential AI-powered tools that can be
incorporated to support the transition to blended learning models, enabling teachers
to gain a good understanding of the existing mainstream AI tools for such a purpose.
Next, we present a case study that demonstrates integrating selected AI tools into a
traditional machine learning course structure, recommending online activities, and
suggesting assignment creation strategies, thereby facilitating a blended learning
model. The chapter concludes by summarizing key findings and reflecting on our
implementation process with critiques.
4.2 Guidelines for Blended Learning Using AI
Our primary objective is to assist teachers in transforming their traditional in-person
lectures into an online format, thereby facilitating the development of a blended
learning course. Informed by research from Abbott et al. (2006), Osgerby and Rush
(2015), Ebner et al. (2020a), and Holt (2023), we have established a teacher-supported
categorisation comprising four key stages to guide the utilisation of diverse AI tools
in supporting the transition to blended learning (Fig. 4.1). These four key stages
are: teacher practice support, online classroom support, evaluation and feedback,
and student support. We propose that teachers may selectively adopt some of the
components for the transition based on their unique pedagogical requirements.
56 M. Khalil et al.
Fig. 4.1 Guidelines for managing and planning blended learning
4.2.1 Teacher Practice Support (TPS)
The essential phase in designing blended learning involves careful management and
planning of a course, which is achieved by an initial focus on teacher practice support
(Hussein 2015). First, this support is facilitated through the utilisation of tools that
assist in the creation of lesson plans and the development of the course structure.
These tools generally have user-friendly interfaces and features, and they can ensure
that the content fits seamlessly into the educational objectives by simplifying the task
of creating engaging lesson plans. Secondly, in the teacher practice support, there
are resources to help teachers enhance their teaching methods and stay up-to-date
with the subject matter they teach. Last but not least, there are tools that can help
with administrative management aspects that can improve teacher efficiency, such
as tools that may help teachers assess their students’ grades.
4.2.2 Online Classroom Support (OCS)
The courses developed and planned require proper execution and rely significantly on
online classroom support in virtual learning environments (Norberg et al. 2011). This
step can be assisted by certain AI tools such as Typeform (https://www.typeform.
com/) and Large Language Models (LLMs). These tools assist in developing activity-
specific content and generating questions and explanation forms allowing students
and teachers to interact. This interaction is beneficial as it provides students with
supportive explanations of subject matters, assists in problem-solving, and enhances
overall student engagement with the course.
4 How to Plan and Manage a Blended Learning Course Module Using 57
4.2.3 Evaluation and Feedback (EF)
Evaluation is a prerequisite to assess the student’s understanding and their engage-
ment with the course (Mandernach 2015). The tools used for both evaluation and
feedback help to track and monitor students’ performance. For example, Audience-
Response-Systems can be used (Haintz et al. 2014) or feedback tools like ‘got Feed-
back’ (https://www.gotfeedback.com/) etc. Such tools provide valuable insights into
individual progress, enabling educators to tailor their approach, address learning
gaps, and create a better personalised learning experience (Bowyer and Chambers
2017).
4.2.4 Student Support (SS)
For the success of blended learning, it is essential to underscore student support
services that address a diverse array of needs (Phillips et al. 2016). Acknowledging
the importance of continual assistance, AI tools can deliver 24/7 tutoring services.
For example, AI-enabled chatbots can provide tailored and timely support to students
and therefore scale student outreach (Nurshatayeva et al. 2020). For students with
special needs, some AI tools support accessibility via for example converting voice
to text in transcripts which as a result offer great help for disadvantaged students in
blended learning classes.
4.3 Landscape of AI-Powered Tools for Blended Learning
Given the rapid evolution of AI and the continuous emergence of new tools, we
conducted a preliminary search for AI tools that can be used for the transition into
blended learning modules. The primary objective of this search was to create a land-
scape of AI tools utilised for structuring and planning courses, as well as managing
resources required for preparing and transitioning the courses online.
To initiate our search, we performed general web searches using Google Web and
academically focused using Google Scholar. The search terms used were AI tool
in education”, AI in blended learning”, and “Hybrid learning and AI tools”. The
emphasis was placed on identifying tools that facilitate the planning and management
of blended learning courses. The review process encompassed information gathered
from various sources, including reviews on different websites and blogs, demos, and
trials of the tools to understand their functionalities, as well as recommendations and
feedback from instructors and learners who have hands-on experience with these
tools available on the web.
Following the survey search, we filtered (N =20) AI tools shown in Table 4.1.
Our selection of tools is based on two indicators: representativeness (the tool is
58 M. Khalil et al.
a representative product of its category and has accumulated a significant number
of users) and accessibility (the tool is easier to obtain, even if it is a paid product
with a free trial period). Each tool underwent a comprehensive evaluation to gauge
its pertinence and intended application within the framework of blended learning.
Specifically, we systematically catalogued each tool, including:
Name: provides the AI tool name
Potential purpose: describes the function of the AI tool in the context of blended
learning.
Web link: provides a web link to access the AI tool.
Accessibility status: Indicates whether the tool is open access or has specific access
restrictions.
Tool classification: we classified the tools based on their primary functions, such
as managing, planning, and/or generating materials required for blended learning.
Alignment with the guideline category: associate the AI tool with the category
from the established guidelines from Fig. 4.1, teacher practice support, online
classroom support, evaluation and feedback, and student support.
When mapping the tools based on their classification, the process involved three
main parts.
Generator—Used to generate text, pictures, slides. The generator helps to create
teaching or learning materials such as text, pictures (infographics), and topic-
specific slides according to the requirements of teachers and students.
Planner—Used for the planning of interactive lessons for students, wherein topics
can be input, and custom lesson plans with quizzes, polls, and other engaging
activities are generated by the AI.
Management—Used to create syllabus and lesson plans. Management AI is used
to manage the lessons and create individualised education program plans.
Table 4.1 presents an overview of the surveyed AI tools. A significant portion of
these tools is available at no cost. However, approximately 95% of them mandate
user account creation for feature access. Furthermore, our analysis indicates that
40% of the surveyed tools offer premium services, requiring a paid subscription for
full functionality. The inclusion of diverse tool types and cost structures ensures that
educators can explore a range of options based on their specific needs and preferences.
The majority of AI tools (65%) fall within the generator class, followed by plan-
ners, which account for 55% of the tools. Finally, classifiers constitute 50% of
Management class in the surveyed AI tools.
In accordance with the guidelines, as depicted in Fig. 4.1, the categorisation of AI
tools into TPS, OCS, EF, and SS shows that a predominant proportion of the surveyed
tools align with TPS, comprising 75%. Subsequently, EF accounts for 45%, while
OCS and SS exhibit equal distribution, each representing 30% per each.
4 How to Plan and Manage a Blended Learning Course Module Using 59
Table 4.1 Selection of AI tools to aid transitioning to blended learning
# AI tool name Potential
purpose
Link Accessibility
status
Tool
classification
Blended learning
guideline category
TPS OCS EF SS
1Stable
diffusion
To generate
needed
pictures for
teaching and
learning
https://
stabledif
fusion
web.
com/
Free,
account
required
Generator (text
to picture)
×
2gotFeedback To ge t
feedback for
assignments
https://
fee
dback.
gotlea
rning.
com/
Free,
account
required
Generator (text
to text)
× ×
3 Nearpod To ma k e
interactive
lectures
including
polls and
quizzes
https://
nea
rpod.
com/
Free,
account
required
Planner ×
4Curipod To ma k e
lesson,
lectures or
slides
https://
curipod.
com/
Free,
account
required,
extra
features
require paid
service
Planner ×
5 Eduaide To ma k e
lesson plan,
teaching
resources
https://
www.
eduaid
e.ai/
Free,
Account
required
Planner,
management
×
6Slidesgo To ma k e
slides for the
course
https://
sli
desgo.
com/
Free,
account
required,
extra
features
require paid
service
Generator ×
7 Audiopen To transcribe
voice to text
https://
audiop
en.ai/
Free,
account
required
Generator ×
(continued)
60 M. Khalil et al.
Table 4.1 (continued)
# AI tool name Potential
purpose
Link Accessibility
status
Tool
classification
Blended learning
guideline category
TPS OCS EF SS
8Typeform To generate
surveys and
questionnaires
https://
www.
typ
eform.
com
Free,
account
required,
extra
features
require paid
service
Generator × × ×
9Quizizz To create and
share quizzes
https://
quizizz.
com/
Free,
account
required
Planner,
management,
and generator
×
10 Bard
(recently
called
Gemini)
To help with
intelligent
assisted
instruction,
contextual
simulations
and create
personalised
learning
experiences
https://
gemini.
google.
com/app
Free,
account
required
Generator
(mainly focus on
text-to-text),
planner, and
management
× × × ×
11 ChatGPT To help with
intelligent
assisted
instruction,
contextual
simulations
and create
personalised
learning
experiences
https://
chat.ope
nai.com
Free,
account
required,
extra
features
require paid
service
Generator
(mainly focus on
text-to-text),
planner, and
management
× × × ×
12 Claude To summarise
text from
word
documents,
PDFs and
more
https://
cla
ude.ai
Free,
account
required,
extra
features
require paid
service
Planner × × ×
(continued)
4 How to Plan and Manage a Blended Learning Course Module Using 61
Table 4.1 (continued)
# AI tool name Potential
purpose
Link Accessibility
status
Tool
classification
Blended learning
guideline category
TPS OCS EF SS
13 Class
Companion
To pr o vid e
personalised
AI feedback
for written
assignments
https://
classc
omp
anion.
com/
Free,
account
required
Management ×
14 Canva Magic
write
To generate
context for
learning and
teaching
https://
www.
canva.
com/
magic-
write/
Free,
account
required,
extra
features
require paid
service
Generator
(text-to-text)
×
15 Gradescope To gi ve
AI-assisted
grading
https://
www.
grades
cope.
com/
Not free,
account
required
Management × ×
16 Fetchy To generate
lesson plans,
plan student
engagement
activities, and
create
learning
communities
https://
www.fet
chy.
com/
Not free,
account
required
Planner,
management,
and generator
(text-to-text)
×
17 Perplexity To help with
intelligent
assisted
instruction,
contextual
simulations
and create
personalised
learning
experiences
https://
www.
perple
xity.ai/
Free,
account
required
Generator
(mainly focus on
text-to-text),
planner,
management
× × × ×
(continued)
62 M. Khalil et al.
Table 4.1 (continued)
# AI tool name Potential
purpose
Link Accessibility
status
Tool
classification
Blended learning
guideline category
TPS OCS EF SS
18 PDF.ai To summarise
Pdfs
http://
www.
pdf.ai/
Free for 1
file with
maximum
file size
10 MB,
account
required,
extra
features
require paid
service
Generator × ×
19 MagicSchool
AI
To ma k e
customised
courses
https://
www.
magics
cho
ol.ai/
Free,
account
required,
extra
features
require paid
service
Planner, and
management and
generator
×
20 CourseAI To ma k e
courses,
quizzes
https://
app.cou
rseai.
com/
Not free,
account
required
Planner,
management and
generator
×
4.4 Case Study: Blended Learning of a Machine Learning
Course
To demonstrate the use of AI tools to support the transition to blended learning
of traditional courses, this section presents a case study of converting one of the
modules of a machine learning course to make it accessible to students online. Our
emphasis was to support teachers who have similar experiences with their current
courses to foster a seamless combination of traditional teaching methods and online
learning components. Through this exploration, we aim to illuminate the practical
steps and considerations involved in leveraging AI tools for a more adaptive and
enriched educational approach.
4.4.1 Course Overview
The machine learning course is offered at the University of Bergen in Norway, partic-
ularly for Ph.D. candidates and voluntarily for master’s students who are interested
in the topic. The course aims to provide a comprehensive introduction to machine
4 How to Plan and Manage a Blended Learning Course Module Using 63
learning, encompassing both theoretical foundations and practical applications. The
course is structured into three parts. The initial segment offers a concise introductory
lecture on the software “R” which provides hands-on examples for students to learn
the needed knowledge to move forward with the course.
The second part focuses on the background information of machine learning,
exploring its capability to extract knowledge from data. Emphasis is placed on the
pivotal role of data in fuelling machine learning endeavours. Topics covered include
data management, encompassing the organisation, storage, cleansing, filtration, and
preparation of data utilised in research projects. The exploration extends to various
aspects of machine learning, including data as a source for future decision-making,
as well as supervised and unsupervised learning techniques. The third part involves
a collaborative mini-group project and the reflection note of the course.
The general learning objective of this course was to enable the students to concep-
tualise the different types of machine learning and filter, manage, and clean data as
well as apply machine learning techniques to datasets from their own context.
To accomplish the objective of this course and pass, students should attend 80%
of the course lectures, complete assignments and reading materials, work in groups,
and submit their machine learning mini-project and reflection notes.
4.4.2 Course Restructuring for Blended Learning
TPS—Lesson plan and generating feedback: We attempted to employ one of the
AI tools, Google Bard (by the date to publish, it has been changed to the name of
Gemini) for scheduling a three-hour portion of our machine learning course. Specifi-
cally, we used the prompt Module structure for the course Introduction to practical
machine learning to Google Bard. This was done to help plan different sessions for
a synchronous session, suggest breaks, and outline module objectives. As shown in
Fig. 4.2, the AI tool Google Bard offered a thorough module plan, encompassing a
structured approach with allocated time slots: 15 min for introduction, 15 min for
syllabus overview, and another 15 min for outlining general course objectives. This
was followed by a 45 min segment dedicated to the discussion of Datafication, a
concept covered in our machine learning course that pertains to representing various
aspects of our lives as data points. Subsequently, a 15-min break was suggested,
followed by a 60 min session on machine learning techniques. Google Bard then
recommends allocating 20 min for hands-on activities and addressing questions and
answers.
We employed the AI tool Eduaide AI to design engaging activities related to
datafication (refer to Fig. 4.3). Proposed activities include conducting quick polls,
showcasing interactive visualisations, and exploring case studies. Eduaide AI also
suggests diverse student-centric learning methods, such as formulating simple ques-
tions, brainstorming deeper inquiries, and applying scenario-based questions. As
teachers, we believe that building a repertoire of activities allows for flexible delivery,
64 M. Khalil et al.
Fig. 4.2 Suggested module structure by Google Gemini (previously called Bard)
4 How to Plan and Manage a Blended Learning Course Module Using 65
Fig. 4.3 Suggested engagement activities by Eduaide AI
either synchronously or asynchronously. The latter of which to integrate selected
activities into one’s course learning management system at their institution.
4.4.3 Planning and Management of Assignments
TPS—Admin support: To gauge students’ prior knowledge, we employed an AI-
powered tool called ‘Typeform’ to administer a pre-course survey. We prompted
Typeform with the following command Ph.D. students’ knowledge about supervised
learning and requested five questions. The resulting pre-survey consisted of a series
of questions designed to assess students’ familiarity with machine learning concepts
and their comfort level with the programming language of instruction. Figure 4.4
provides a visual screenshot of the suggested survey questions proposed by Typeform.
66 M. Khalil et al.
Fig. 4.4 Suggested pre-survey questions by Typeform
The insights gleaned from this survey can enable us to tailor the course content to
align with the specific needs and learning styles of our diverse group of participants.
OCS—Explanations: Another example we used for managing assignments is Class-
room Companion which is a tool that can be used to optimise the educational experi-
ence for both teachers’ management and planning. By offering features that facilitate
collaboration, constructive feedback, and assignment administration, this tool helps
create an interactive and supportive online classroom support. We employed Class-
room Companion tool to facilitate collaboration between teachers and students by
providing feedback on students’ answers which help teachers to evaluate their level
of understanding.
4 How to Plan and Manage a Blended Learning Course Module Using 67
Fig. 4.5 An example of an auto-generated assignment by ‘Classroom Companion’
We prompted the tool to provide several questions to be posted for several groups
in online discussions. For example, one of the automated questions generated from
the tool is Describe how machine learning is applied in a domain of your choice.
List and explain practical uses of supervised machine learning. A student from
University of Bergen with random ID UIB305296 5002 offered a thoughtful response
to the question. Teachers can enable the tool to also provide feedback (see Fig. 4.5).
We anticipate that AI-powered platforms have the potential to enhance the
online learning experience, offering instructors the opportunity to integrate adap-
tive learning mechanisms and streamline assignment management. Teachers can
conveniently create, publish, and grade assignments, fostering a more engaging and
interactive learning environment.
EF-Evaluation and Feedback: We used Typeform to create an evaluation and feed-
back form for assessing our blended learning approach. We instructed the AI tool
to generate eight questions, focusing on evaluating the course structure, syllabus,
overall learning experience, and inviting students to share any additional thoughts
for future improvements. Additionally, we requested the tool to compile and report
the results back to us. Interestingly, Typeform generated seven questions related to
the course evaluation and included one question for providing contact details, as
outlined below:
Provide the email address.
How satisfied are you with the machine learning course? (Likert Scale).
What describes your experience with the blended learning form of the course?
(Multiple-choice question).
68 M. Khalil et al.
Fig. 4.6 An example of PDF.ai for student support
How would you rate the content of the machine learning course? (Likert Scale).
How do you rate the structure of the course (Likert scale).
What did you like the most about the course blended learning format? (open text).
What you did not like the most about the course? (open text).
Do you have other suggestions for improving the course? (open text).
SS—24/7 tutor: In order to enable independent learning, students often need guidance
from 24/7 tutoring services. Here students can use an AI tool named PDF.ai to know
more about the subject and the contents. Pdf version of one of the books in machine
learning Understanding Machine Learning: From Theory to Algorithms was used
in the tool PDF.ai (as in Fig. 4.6). The students were able to extract information
from the book and get answers to the question Difference between supervised and
unsupervised learning”. As per the Guideline in Fig. 4.1, this tool was used for
student support.
4.5 Conclusions
One of the pivotal modules that has gained prominence in the post-pandemic times
is blended learning. The format of content delivery via blended learning mainly
entailed two components, offline and online. While both components are applicable
for technology endorsement, the online component, provided its nature of being
digital and datafied, offers greater opportunities for incorporating technology.
In this chapter, we aimed to assist teachers in transitioning to blended learning by
providing a categorisation of potential AI tools and their prospective applications in
blended learning. In our guideline for the usage of AI tools, we identified four key
areas for an AI-mediated blended learning: teacher practice support, online classroom
4 How to Plan and Manage a Blended Learning Course Module Using 69
support, evaluation and feedback, and student support. We allocated for each of these
areas particular AI tools that can be used to support teachers for this transition as
shown in Fig. 4.1 and Table 4.1. We showcased an example from one of the courses
we teach and demonstrated how to create automatic assignments for students and get
a list of suggestions for activities to carry out in a blended learning mode.
While recognizing the significant potential of employing AI technologies in
teaching and transitioning to hybrid and blended learning modes, we acknowledge
the necessity of taking specific steps to:
Educate teachers about the benefits of AI: Emphasise the potential of AI to enhance
teaching and learning and provide teachers with examples of successful AI-based
education systems.
Provide teachers with training and support to use AI applications: We envisage
that teachers require opportunities to learn about AI and develop the skills they
need to contribute to its development (i.e., AI literacy).
Create accessible AI tools and resources: Make AI tools and resources readily
available to teachers by their institutions, schools, and workplaces and provide
them with training on how to use them effectively in their instructions.
Continuous monitoring and evaluation: Implement mechanisms for continuous
monitoring and evaluation of AI applications in education. Regularly assess
their impact on student learning outcomes, teacher effectiveness, and overall
educational quality.
We strongly like to express here that teachers must understand the concept of
‘Blended Learning’ and how to transform traditional lectures to an appropriate online
enhanced one. Therefore, guidelines were introduced, and a 4-step model presented:
Teacher practice support
Online classroom support
Evaluation and Feedback
Student support
Based on the model different AI tools and applications can support and assist
teachers, nevertheless there is a strong need of appropriate digital skills in the domains
of Blended Learning and AI.
We also highlight several limitations associated with the conversion to blended
learning through the usage and employment of AI applications for teaching.
Primarily, a significant number of these tools are commercially available, which
requires teachers and their institutions to invest in AI tool packages for digitalising
their courses or modules. The financial aspect poses a considerable constraint, given
the substantial costs associated with these services.
Another identified limitation is the mandatory creation of user accounts for many
AI tools, consuming valuable time and subjecting teachers to an influx of promotional
emails and spam.
Additionally, many of the available AI tools rely on powerful engines, such as
OpenAI ChatGPT, potentially resulting in a lot of repetitive content creation and
redundancy of instructions for structuring courses and assignments.
70 M. Khalil et al.
We also think that there might be scepticism among students regarding their
teachers’ use of AI tools, which raises concerns about the perceived reliability of
pedagogical, didactical, and educational practices by the teachers.
Moreover, privacy concerns will emerge whether now or then, questioning where
students’ responses are stored, who has access to them, and whether consent is
required for utilising such AI tools. We believe tensions arise in the context of schools,
potential requirements for institutional consent from parents and considerations for
students opting out of these services also warrant attention, along with associated
management costs for such actions.
To conclude this chapter, we underscore the significant role of AI in enhancing
the planning, management, implementation, and assessment of new courses. This
finding aligns with the research conducted by Celik et al. (2022), who demonstrated
that AI offers teachers a multitude of opportunities to optimise various aspects of
teaching and learning.
Questions for Reflection
1. What are the key elements of using generative AI in blended learning? How the
guidelines can be used in your own context?
2. Do you find the list of tools useful? Are there other such tools that you can use?
3. Blended learning requires both online and offline activities. How do you think
it’s important to ensure fairness and inclusivity when using generative AI tools
in this context?
4. What other guidelines do you think is important to consider when employing AI
in supporting blended learning?
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