sustainability

Article
Empowering Education with Generative Artificial Intelligence
Tools: Approach with an Instructional Design Matrix
Lena Ivannova Ruiz-Rojas 1 , Patricia Acosta-Vargas 2,3, * , Javier De-Moreta-Llovet 4
and Mario Gonzalez-Rodriguez 2,5, *

1 Departamento de Ciencias Humanas y Sociales, Universidad de Las Fuerzas Armadas,

Sangolquí 170550, Ecuador; liruiz@espe.edu.ec
2 Intelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito 170125, Ecuador
3 Carrera de Ingeniería Industrial, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas,
Quito 170125, Ecuador
4 Facultad de Derecho, Universidad Complutense de Madrid, 28040 Madrid, Spain; jamoreta@ucm.es
5 Carrera de Ingeniería de Software, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas,
Quito 170125, Ecuador
* Correspondence: patricia.acosta@udla.edu.ec (P.A.-V.); mario.gonzalez.rodriguez@udla.edu.ec (M.G.-R.)

Abstract: This study focuses on the potential of generative artificial intelligence tools in education,
particularly through the practical application of the 4PADAFE instructional design matrix. The
objective was to evaluate how these tools, in combination with the matrix, can enhance education and
improve the teaching–learning process. Through surveys conducted with teachers from the University
of ESPE Armed Forces who participated in the MOOC course “Generative Artificial Intelligence Tools
for Education: GPT Chat Techniques”, the study explores the impact of these tools on education. The
findings reveal that generative artificial intelligence tools are crucial in developing massive MOOC
Citation: Ruiz-Rojas, L.I.; virtual classrooms when integrated with an instructional design matrix. The results demonstrate the
Acosta-Vargas, P.; De-Moreta-Llovet, potential of generative artificial intelligence tools in university education. By utilizing these tools in
J.; Gonzalez-Rodriguez, M. conjunction with an instructional design matrix, educators can design and deliver personalized and
Empowering Education with enriching educational experiences. The devices offer opportunities to enhance the teaching–learning
Generative Artificial Intelligence process and tailor educational materials to individual needs, ultimately preparing students for the
Tools: Approach with an
demands of the 21st century. The study concludes that generative artificial intelligence tools have
Instructional Design Matrix.
significant potential in education. They provide innovative ways to engage students, adapt content,
Sustainability 2023, 15, 11524.
and promote personalized learning. Implementing the 4PADAFE instructional design matrix further
https://doi.org/10.3390/
enhances the effectiveness and coherence of educational activities. By embracing these technological
su151511524
advancements, education can stay relevant and effectively meet the digital world’s challenges.
Academic Editors: Michail
Kalogiannakis, Ching Sing Chai,
Keywords: methodology; MOOC; digital resources; teaching strategies
Thomas K.F. Chiu and
Murod Ismailov

Received: 30 June 2023

Revised: 24 July 2023 1. Introduction
Accepted: 24 July 2023 In today’s digital era, education is undergoing a paradigm shift driven by techno-
Published: 25 July 2023 logical advancements. The integration of generative artificial intelligence (AI) tools and
instructional design matrices represents an innovative and promising approach to address-
ing the evolving needs of modern education. By harnessing the power of AI, educators
can leverage personalized learning experiences, adaptive content generation, and real-time
Copyright: © 2023 by the authors.
support for students. Using an instructional design matrix adds structure and coherence to
Licensee MDPI, Basel, Switzerland.
This article is an open access article
the educational process, ensuring alignment with learning objectives and enhancing the
distributed under the terms and
effectiveness of teaching strategies. This combined approach improves student engagement
conditions of the Creative Commons and motivation and offers educators new opportunities to create dynamic and inclusive
Attribution (CC BY) license (https:// virtual classrooms. By exploring these technologies’ potential benefits and implications,
creativecommons.org/licenses/by/ this paper aims to inspire educators and institutions to embrace generative AI tools and
4.0/). instructional design matrices as transformative tools in empowering education.

Sustainability 2023, 15, 11524. https://doi.org/10.3390/su151511524 https://www.mdpi.com/journal/sustainability

Sustainability 2023, 15, 11524 2 of 20

Education has undergone a significant transformation in today’s rapidly evolving

digital landscape. The integration of generative artificial intelligence (AI) tools and in-
structional design matrices has revolutionized the way learning activities are conceived
and executed [1,2]. The application of AI in education holds immense potential, offering
new possibilities for personalized learning experiences and adaptive teaching approaches.
Simultaneously, the increasing digitization of society has propelled the prominence of
artificial intelligence with its ability to automate tasks, analyze vast amounts of data, and
provide predictive insights that have far-reaching implications across various domains [3].
The stated problem of enhancing education using generative artificial intelligence
tools in a practical approach directly relates to the research question: “How can education
be enhanced using generative artificial intelligence tools in a practical approach, applying
the 4PADAFE instructional design matrix?” 4PADAFE [4] stands for Academic Project,
Strategic Plan, Instructional Planning, Instructional Material Production (4P), Teaching
Action (AD), Formative Adjustments (AF), and Evaluation (E). The research question seeks
to specifically inquire into the possibilities and benefits of generative artificial intelligence
tools, combined with the 4PADAFE matrix, to enhance the teaching–learning process. The
aim is to explore how these tools can personalize learning, provide immediate feedback,
adapt educational materials, and promote the development of key skills in students. By
analyzing and evaluating the potential of these tools in a practical approach, we seek
to identify effective strategies for their implementation in education to optimize the stu-
dent experience and strengthen the quality of education in a digitalized and constantly
evolving environment.
Another problem is the lack of knowledge on the part of teachers of generative artificial
intelligence tools and systematic processes for designing micro-curricular activities that
guide the development and construction of massive virtual learning classrooms; this
would enable teachers to manage activities and design digital resources with artificial
intelligence with innovative educational strategies that facilitate the creation of massive
virtual classrooms [1].
The general objective of this research has been to analyze and evaluate the potential of
generative artificial intelligence tools in the educational context, focusing on the practical
application of the 4PADAFE instructional design matrix.
Based on the stated objectives, the proposed scientific or working hypothesis to be
contrasted or demonstrated in this study is that applying generative artificial intelligence
tools in the educational environment, using the 4PADAFE instructional design matrix,
positively impacts the teaching–learning process.
Based on the statement above, this combination of tools and practical approach is
expected to improve academic results and student engagement in learning, promoting
more efficient, effective, and personalized education.
The present study shows the results of the implementation of the 4PADAFE method-
ology; during the development of the course “Generative Artificial Intelligence Tools for
Education. ChatGPT Techniques”, teachers were given the task of designing a teaching
unit using both the generative artificial intelligence tools (IAG) learned in the course and
the 4PADAFE instructional design matrix.
In practice, the teachers demonstrated a solid understanding and application of the
4PADAFE methodology and the generative artificial intelligence tools. First, they conducted
detailed planning, identifying the specific learning objectives they wanted to achieve in
their teaching unit. From there, they designed micro-curricular activities that aligned with
the principles of the 4PADAFE matrix.
The activities proposed by the teachers involved using IAG tools at different stages of
the educational process. For example, they designed initial activities that took advantage
of the content generation capabilities of IAG tools to present the contents of a subject attrac-
tively. They also designed interactive activities where students interacted with different
IAG tools such as chatPDF.com, You.com, chatbots, and virtual assistants to solve problems,
receive feedback, or explore new ideas.
Sustainability 2023, 15, 11524 3 of 20

In addition, teachers used IAG tools to evaluate student performance more efficiently
and effectively. These tools allowed them to analyze student-generated responses, assess
comprehension, and provide real-time personalized feedback.
In summary, the teachers demonstrated a solid command of the 4PADAFE methodology
and generative artificial intelligence tools. They planned and designed their micro-curricular
activities effectively using IAG tools, leveraging their capabilities to enhance content pre-
sentation, promote student–teacher interaction, and streamline learning assessment. These
combined approaches resulted in dynamic and enriching teaching units that fostered active
student participation and facilitated the achievement of the learning objectives.
In conclusion, current technological educational trends emphasize the use of gen-
erative artificial intelligence tools, which allow the creation of personalized educational
content tailored to the needs of students. In addition, implementing an instructional design
matrix provides a structured guide for developing micro-curricular activities, ensuring
coherence and quality in the educational process.
On the other hand, implementing an instructional design matrix in the educational
process has become a fundamental practice to guarantee quality and coherence in the
design of co-curricular activities. An instructional design matrix provides a structure and a
clear guide for the development of the activities, considering the educational objectives, the
contents, the teaching strategies, the evaluations, and the necessary resources. By following
an instructional design matrix, educators can ensure that activities align with learning
objectives and promote an effective and meaningful educational process [4].
The evolution of generative artificial intelligence raises the need to reconsider the
teaching–learning process since its impact extends to the trend of adaptive education. This
trend can potentially have a significant impact on conventional learning approaches. As
new and better AI-based applications are developed, the new curricula will likely become
more responsive and versatile enough to quickly adapt to the latest and most efficient ways
of approaching education in the present century [5].
This article will explore generative artificial intelligence tools, emphasizing some of
them, such as ChatGPT [6], Fliki Ai [7], You.com [8], Studio.Ai [9], Chat Pdf.Com [10],
Leonardo AI [11], and Humata.ai [12]. These tools use advanced algorithms to create
and generate educational content in an automated way, providing new opportunities for
personalized learning and adaptation to students’ individual needs [13]. The impact of
generative artificial intelligence on the personalization of education will be examined, as
well as how implementing an instructional design matrix can improve the effectiveness
and coherence of the educational process. In addition, examples of good practice will
be presented, and the future implications of these technological educational trends will
be discussed.
This study contributes by exploring the integration of generative AI tools and instruc-
tional design matrices as transformative tools in education. It highlights AI’s potential
to personalize learning, generate adaptive content, and provide real-time support. The
instructional design matrix adds structure and coherence, enhancing teaching strategies
and student engagement. Implementing AI tools and the 4PADAFE matrix in a course
demonstrated their practical application, enabling teachers to design interactive activities,
improve content presentation, and streamline assessment. This integration resulted in
dynamic teaching units that fostered active student participation and the achievement of
learning objectives.
This paper is organized into several sections. In Section 2, readers are introduced to
generative artificial intelligence tools and their application in implementing MOOC courses
using the 4PADAFE methodology. Section 3 describes the research methodology used to
harness artificial intelligence in developing virtual course content. The analysis findings,
which used artificial intelligence and the 4PADAFE methodology in implementing MOOC
courses, are presented in Section 4. A comprehensive discussion of the results is provided
in Section 5, while Section 6 focuses on the implications of the practices. The conclusions
drawn from the research are presented in Section 7.
Sustainability 2023, 15, 11524 4 of 20

implementing MOOC courses, are presented in Section 4. A comprehensive discussion of

Sustainability 2023, 15, 11524 the results is provided in Section 5, while Section 6 focuses on the implications of the4prac-
of 20
tices. The conclusions drawn from the research are presented in Section 7.

2. Generative
2. Generative Artificial
Artificial Intelligence
Intelligence Tools
Toolsandandthe
the4PADAFE
4PADAFEInstructional
InstructionalMatrix
Matrix
Generative artificial
Generative artificial intelligence
intelligence tools
tools automatically
automatically generate
generate personalized
personalized educa-
educa-
tional content using advanced algorithms. These tools analyze student
tional content using advanced algorithms. These tools analyze student data and create data and create
tailored materials, enhancing active participation and motivation for learning.
tailored materials, enhancing active participation and motivation for learning. They allow They allow
the creation
the creation of
of personalized
personalized educational
educational content
content based
based onon individual
individual needs
needs and
and prefer-
prefer-
ences[14].
ences [14].AAScopus
Scopus search
search on “generative
on “generative artificial
artificial intelligence
intelligence tools intools in education”
education” yielded
yielded
39 39 scientific
scientific articles. articles.
Therelated
The relatedkeywords
keywordsformedformed twotwo
mainmain clusters:
clusters: one one focused
focused on artificial
on artificial intelli-
intelligence,
gence, ChatGPT, education, and learning systems; the other on deep learning,
ChatGPT, education, and learning systems; the other on deep learning, human, and machine human, and
machine learning.
learning. This graphical
This graphical representation
representation helps identify
helps identify relevantrelevant
researchresearch
areas forareas
futurefor
future studies
studies in generative
in generative artificial
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intelligence tools tools in education.
in education. Figure
Figure 1 shows
1 shows a neu-
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ral network
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twotwomain main groups
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generated withwith VOSviewer
VOSviewer [15].[15].

Figure1.
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The
The article
article[16]
[16]points
pointsout outthat
thatgenerative
generativeartificial
artificialintelligence
intelligence(AI)(AI)offers
offerstransforma-
transform-
tive
ativepotential
potentialinineducation.
education.This Thisexploratory
exploratorypaper paperapplies
appliesaaself-study
self-studymethodology
methodology to to
investigate this technology. The results of ChatGPT are usually aligned
investigate this technology. The results of ChatGPT are usually aligned with the central with the central
themes
themesof ofthe
thestudy,
study,and
and forfor
this reason,
this reason,it isit essential thatthat
is essential educators
educatorsmodel responsible
model responsibleuse
of
useChatGPT, prioritize
of ChatGPT, critical
prioritize thinking,
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thinking, clear
and be about expectations.
clear about ChatGPT
expectations. is likely
ChatGPT is
helpful for educators
likely helpful in designing
for educators sciencescience
in designing units, rubrics, and quizzes.
units, rubrics, and quizzes.
Another
Another study [17] suggests
study [17] suggeststhat thatfuture
futureteachers
teachersshould
should have
have thethe opportunity
opportunity to
to ac-
access courses focused on applying artificial intelligence and integrating
cess courses focused on applying artificial intelligence and integrating technology in the technology in
the classroom
classroom as anasintegral
an integral
part of part ofinitial
their their training.
initial training.
However, However,
students students need to
need to recognize
recognize that they have primary responsibility as learners and should
that they have primary responsibility as learners and should not rely solely on AI appli- not rely solely on
AI applications, such as ChatGPT, to fulfill their
cations, such as ChatGPT, to fulfill their educational role. educational role.
One
One ofof the
the problems
problems identified
identified isis the
the lack
lackof ofknowledge
knowledge aboutabout generative
generative artificial
artificial
intelligence
intelligence tools and systematic processes for designing co-curricular activities thatguide
tools and systematic processes for designing co-curricular activities that guide
the
the development
development and and construction
construction of of massive
massive virtual
virtual learning
learning classrooms;
classrooms; these
these allow
allow
teachers to manage activities and digital resources with artificial intelligence and educa-
tional strategies to create their virtual classrooms. In this study, the 4PADAFE methodology
makes it possible to align the learning objectives, contents, activities, and evaluations,
guaranteeing the coherence and quality of the instructional design.
Sustainability 2023, 15, 11524 5 of 20

The supply of “smart” applications with possible educational and/or academic uses is
experiencing constant growth. New options are added daily to the wide range of generative
AI tools available. Websites and specialized portals such as Futurepedia or All Things AI
provide access to various tools. These resources allow educators and academics to explore
and harness the potential of generative artificial intelligence to enrich their pedagogical
practices and promote more interactive and personalized learning [18].
The influence of generative artificial intelligence in university education has been
significant in recent years. The ability of artificial intelligence tools to automatically generate
educational content has transformed how education is delivered and accessed at the
university level [19]. Some relevant artificial intelligence tools and their importance in
university teaching are described below [20].
ChatGPT is an artificial intelligence tool that uses generative language models to
interact and answer questions conversationally [6]. This tool allows teachers to use it as a
virtual assistant to provide answers to student queries, offer additional information, and
provide personalized support in real time.
Fliki AI is an artificial intelligence tool [7] designed to create educational content. It
allows for generating didactic materials, such as interactive presentations, quizzes, and
adaptive learning activities. University faculty can use Fliki AI to develop high-quality,
personalized educational resources tailored to the needs of their students.
You.com is a search engine that combines search results, applications, and shortcuts to
present information in an organized and easy-to-use way.
Aistudio.com is an artificial intelligence video generation platform that uses an AI
avatar. It allows to generate realistic AI videos quickly and efficiently [21].
Chat Pdf.com is a system based on artificial intelligence that allows us to “read” and
synthesize the most important ideas and returns a complete summary of any document
in PDF format. One of its features is that it understands any language and can reply in
the chosen language. It is used to propose co-curricular activities; we can use the tool to
summarize documents and then use the summaries to pose discussion questions or assign
writing activities.
Leonardo AI is an artificial intelligence tool that uses computer vision and machine
learning to analyze images and videos. In the university context, teachers can use Leonardo
AI to enhance the teaching experience, such as identifying objects in scientific experiments
or interpreting medical images in the medical field.
Humata.ai is an artificial intelligence platform that uses machine learning algorithms
to analyze course content and provide personalized recommendations to college students.
Teachers can use Humata.ai to adapt the content and teaching methodology to the needs of
the students, improving the learning experience.
These artificial intelligence tools are transforming college education by providing
new opportunities to personalize learning, improve the quality of content, and facilitate
interaction between students and faculty. They allow educators to adapt to the needs of
students, offering a more personalized and practical approach to teaching and learning [22].
Properly implementing these tools requires careful planning and effective integration
into the instructional design. University professors must receive adequate training on using
these tools effectively and maximizing their potential to benefit students.
The present methodology proposes systematic cyclical processes to develop a mas-
sive virtual course efficiently. It should allow the construction of didactic material and
the development of activities in an orderly and structured manner while responding to
the application of innovative pedagogical bases and learning resources for a MOOC. Its
interactive component facilitates training adjustments, which aligns with the idea that,
even when everything has been planned, adjustments are possible to accommodate the
specific group one happens to be working with [23]. Figure 2 presents an infographic of the
4PADAFE methodology.
Sustainability 2023, 15, 11524 6 of 20

when everything has been planned, adjustments are possible to accommodate the specific
group one happens to be working with [23]. Figure 2 presents an infographic of the 4PA-
Sustainability 2023, 15, 11524 6 of 20
DAFE methodology.

Figure 2. 4PADAFE Methodology.

Figure 2. 4PADAFE Methodology.
The methodology proposed in this study is based on integrating the principles and
combining socio-constructivist
The methodology proposed andin connectivist
this study ismodels.
based onIt focuses on effective
integrating communica-
the principles and
tion and engages students in open learning and reflective inquiry [23].
combining socio-constructivist and connectivist models. It focuses on effective communi-
The 4PADAFE methodology, which consists of seven phases, is a reference for this
cation and engages students in open learning and reflective inquiry [23].
study. Phase 1 involves the eLearning plan and the academic teaching project, followed
The 4PADAFE methodology, which consists of seven phases, is a reference for this
by the strategic plan (Phase 2), instructional planning (Phase 3), production of didactic
study. Phase 1 involves the eLearning plan and the academic teaching project, followed
material (Phase 4), teaching action (Phase 5), training adjustments (Phase 6), and evaluation
by the strategic plan (Phase 2), instructional planning (Phase 3), production of didactic
(Phase 7) [2].
material (Phase 4), teaching action (Phase 5), training adjustments (Phase 6), and evalua-
The concept of “techno-instructional or techno-pedagogical design” recognizes the
tion (Phase 7) [2].
inseparable connection between the technological and pedagogical dimensions in virtual
The concept of “techno-instructional or techno-pedagogical design” recognizes the
training. The technological dimension involves selecting appropriate tools, such as virtual
inseparable connection between the technological and pedagogical dimensions in virtual
platforms, software applications, and multimedia resources, while the pedagogical dimen-
training. The technological dimension involves selecting appropriate tools, such as virtual
sion requires understanding the target audience, analyzing objectives or competencies,
platforms,
developing software applications,
and implementing and multimedia
content, resources,
planning activities, andwhile the pedagogical
evaluating processes anddi-
mension requires
results [24]. understanding the target audience, analyzing objectives or competen-
cies, developing and and
These concepts implementing
methodologiescontent,
serveplanning activities,
as a framework forand evaluating
instructional processes
design in vir-
and
tualresults [24]. to effectively integrate technology and pedagogy to enhance the
training
Theseexperience.
learning concepts and methodologies serve as a framework for instructional design in
virtualThetraining to effectively
development starts integrate
with Phase technology and pedagogy
2 of the instructional to enhance
design matrix tothe learning
address the
experience.
pedagogical dimension. Figure 3 outlines the following key points to be considered:
The development
(a) Select a subject starts with
or area Phase
that 2 ofwith
aligns the instructional design(b)
the desired profile. matrix to address
Choose the
the specific
pedagogical dimension. Figure
units to be developed. 3 outlines
(c) Identify one the following
or two topics key
per points
unit forto developing
be considered: learning,
(a) Select
didactic, and adigital
subjectstrategies.
or area that
(d)aligns with
Clearly the desired
state profile.
the objective for(b) Choose
each unit.the
(e)specific
Considerunits
the
totopics
be developed.
that will be covered in each unit. (f) Define the expected learning outcome for and
(c) Identify one or two topics per unit for developing learning, didactic, each
unit.
These guidelines provide a systematic approach to designing the pedagogical aspect
of the instructional design matrix, ensuring a structured and effective learning process.
Sustainability 2023, 15, 11524 7 of 20

digital strategies. (d) Clearly state the objective for each unit. (e) Consider the topics that will
be covered in each unit. (f) Define the expected learning outcome for each unit.
Sustainability 2023, 15, 11524 7 of 20
These guidelines provide a systematic approach to designing the pedagogical aspect
of the instructional design matrix, ensuring a structured and effective learning process.

Figure 3.
Figure 3. 4PADAFE instructional design
4PADAFE instructional design matrix
matrix methodology
methodology for
for designing
designing aa virtual
virtual course.
course.

the context
In the contextofofvirtual
virtualtraining,
training, various
various generative
generative artificial
artificial intelligence
intelligence tools,
tools, in-
includ-
cluding
ing ChatChat
GPT,GPT, FlikiYou.com,
Fliki Ai, Ai, You.com, Aistudio,
Aistudio, Chat Pdf.com,
Chat Pdf.com, Leonardo Leonardo
Ai, andAi, and Hu-
Humata.ai,
among
mata.ai,others,
amongcoupled with thewith
others, coupled utilization of an instructional
the utilization design matrix,
of an instructional enable the
design matrix, en-
enhancement and personalization
able the enhancement of learning
and personalization experiences.
of learning These tools
experiences. Theseandtools
methodologies
and meth-
offer moreoffer
odologies effective
moreand enriching
effective and educational experiences
enriching educational for students.
experiences for students.
The 4PADAFE methodology, which supports the design of virtual virtual classrooms,
classrooms, incor-
incor-
porates technological resources and generative artificial intelligence tools to plan training
activities. Technology
Technology servesserves as
as a means to fulfill academic planning, and teachers must
select appropriate technological
technological strategies
strategies toto ensure
ensure thethe success
success of of the
the learning
learning process.
process.
Educators
Educators must must be beknowledgeable
knowledgeableabout aboutandandproficient
proficientinin applying
applying AIAI tools and
tools anddigital re-
digital
sources
resources to create
to createdigital content.
digital This This
content. development
developmentoccursoccurs
in parallel with Phase
in parallel with3,Phase
Phase 3, 5,
and Phase 7 of the instructional design matrix, as illustrated in Figure
Phase 5, and Phase 7 of the instructional design matrix, as illustrated in Figure 3. 3.
When
When designing synchronous and
designing synchronous and asynchronous activities, it
asynchronous activities, it is
is advisable
advisable to to utilize
utilize
the instructional design matrix of the 4PADAFE methodology.
the instructional design matrix of the 4PADAFE methodology. Consider the following Consider the following
guidelines:
guidelines: (1) (1)Employ
Employcaptivating
captivatingvideo
videoclasses
classesthatthatcreate
create a “WOW”
a “WOW” effect,
effect, captur-
capturing
ing and maintaining students’ attention. (2) Utilize diverse learning
and maintaining students’ attention. (2) Utilize diverse learning strategies tailored to the strategies tailored
to the specific
specific target audience.
target audience. (3) strategies
(3) Choose Choose strategies
that alignthat withalign with the
the subject subject
matter, matter,
adjusting
adjusting the complexity level as students progress in their knowledge.
the complexity level as students progress in their knowledge. (4) Apply motivational strat- (4) Apply motiva-
tional
egies, asstrategies,
motivation as motivation
and learning and
arelearning are interrelated
interrelated with academic with academic performance.
performance. (5) Empha-
(5) Emphasize that the teacher’s effectiveness lies in their
size that the teacher’s effectiveness lies in their knowledge of the subjectknowledge of the subject
matter andmatter
their
and their mastery of various didactic strategies and generative artificial intelligence tools.
(6) Utilize learning strategies and AI-powered tools to promote students’ gradual autonomy.
Teachers must define innovative working methods by leveraging generative artificial
intelligence tools and digital resources to enhance the learning experience.
 Sustainability 2023, 15, 11524 8 of 20

mastery
mastery ofof various
various didactic
didactic strategies
strategies and
and generative
generative artificial
artificial intelligence
intelligence tools.
tools. (6)
(6) Utilize
Utilize
learning
learning strategies
strategies and
and AI-powered
AI-powered tools tools to
to promote
promote students’
students’ gradual
gradual autonomy.
autonomy.
Sustainability 2023, 15, 11524 8 of 20
Teachers
Teachers must
must define
define innovative
innovative working
working methods
methods by by leveraging
leveraging generative
generative artificial
artificial
intelligence
intelligence tools
tools and
and digital
digital resources
resources to
to enhance
enhance thethe learning
learning experience.
experience.
Figure
Figure 44 describes
describes thethe building
building ofof micro-curricular
micro-curricular activities
activities with
with the
the 4PADAFE
4PADAFE in- in-
Figure design
structional
structional 4 describes
design theIt
matrix.
matrix. Itbuilding
shows
shows howof micro-curricular
how to
to design
design aa Phase
Phase activities with
22 strategic
strategic theIn
plan.
plan. 4PADAFE
In this in-
this section,
section,
structional
the
the contentsdesign
contents are matrix. It into
are structured
structured shows how tounits;
into didactic
didactic design
units; thea unit
the Phase is2organized
unit is strategic plan.
organized by In thisand
by weeks,
weeks, section,
and each
each
the contents
subject’s
subject’s are structured
specific
specific and
and genericintocompetencies,
generic didactic units;objectives,
competencies, the unit is organized
objectives, and
and learning by weeks,
learning outcomes
outcomes andare
each
are sub-
identi-
identi-
ject’s
fied.
fied. specific and generic competencies, objectives, and learning outcomes are identified.

Figure 4.
Figure4.
Figure 4PADAFE
4.4PADAFE methodology
4PADAFEmethodology for
methodologyfor creating
creatingaaavirtual
forcreating virtual course,
virtualcourse, phase
phase2.2.
course,phase 2.

Figure
Figure555shows
Figure showsthe
shows theproposed
the proposedsynchronous
proposed synchronous(online)
synchronous (online) and
(online) and asynchronous
and asynchronous (offline)
(offline) activ-
(offline) activ-
ities
ities and
ities and how
and how to
how to transform
to transform a boring
transform aa boring class
boring class into
class intoa fun,
into aa fun,motivating
fun, motivating class
motivating class with
withfeedback.
class with feedback. ItIt
feedback. It
should
shouldbe
should benoted
be notedthat
noted thatto
that toautomate
to automatethe
automate theco-curricular
the co-curricularactivities,
co-curricular activities,an
activities, anLMS,
an LMS,aaavirtual
LMS, virtuallearning
virtual learning
learning
environment,
environment,isis
environment, isneeded.
needed.
needed.

Figure 5.
Figure5.
Figure 4PADAFE
5.4PADAFE methodology
4PADAFEmethodology for
methodologyfor creating
creatingaaavirtual
forcreating virtual course,
virtualcourse, phase
phase3.3.
course,phase 3.

Creating
Creatingaaavirtual
Creating virtualclassroom
virtual classroomwithin
classroom within
withina avirtual learning
a virtual
virtual learningenvironment
learning involves
environment
environment involves
involvesfollowing
follow-
follow-
the
inginstructional design matrix described in Figure 5. The matrix emphasizes the
ing the instructional design matrix described in Figure 5. The matrix emphasizes the
the instructional design matrix described in Figure 5. The matrix importance
emphasizes the
of strategic planning (Phase 2), instructional planning (Phase 3), teaching actions (Phase 5),
and evaluation (Phase 6).
Phase 2 focuses on developing micro-curricular planning for each unit, including defin-
ing competencies, objectives, contents, and learning outcomes every week. Phase 3 builds
upon Phase 2 and involves creating study materials using digital resources, interactive mul-
timedia technologies, and virtual learning environments. Synchronous and asynchronous
Sustainability 2023, 15, 11524 9 of 20

learning activities are planned, with the learning organization determined by the number of
hours allocated to the teaching component (CD), application and experimentation practices
(CPA), and autonomous learning (CAA).
CD’s teaching component encompasses direct teacher interaction, collaborative learn-
ing activities, video lectures, forums, discussions, joint exercises, and feedback. Digital
resources such as videos, videoconferences, podcasts, presentations, concept maps, and dig-
ital books are utilized. The application and experimentation practices component involves
practical or laboratory activities complementing the study material. Autonomous learning
consists of activities independently developed by students under teacher guidance. These
components lead to Phase 5, where integrative activities or final products are proposed
to achieve the training objectives. Phase 4 involves designing didactic materials based on
micro-curricular planning, while Phase 6 defines the number of assessments per week.
Various educational resources can be employed to enhance student learning, including
audiovisual materials, which should be concise (no longer than 5 min), with high image
and sound quality, and accompanied by a well-developed script. Combining different
types of educational resources can increase student interest and motivation. It is essential
to ensure compliance with accessibility requirements and creative commons licenses for
these resources.
In online training, teachers fulfill new roles supported by generative artificial intelli-
gence and digital strategies. This methodology includes using digital tools and resources
to design engaging educational materials, conducting synchronous and asynchronous
tutoring sessions, facilitating academic forums, providing guidance on technology usage,
monitoring the learning process, evaluating student progress, and providing feedback.
Continuous evaluation processes are carried out using formal and informal tools and
evaluation rubrics.
These methodologies and strategies aim to optimize the online learning experience,
recognizing virtual education’s unique characteristics and advantages.
Micro-curricular planning in virtual environments requires teachers to possess peda-
gogical knowledge, didactic skills, and digital expertise [25]. When implementing artificial
intelligence activities, the following steps are suggested [19,26]:
Identify areas where artificial intelligence can be beneficial, such as providing person-
alized feedback or analyzing student work for progress assessment.
Explore available AI tools and resources suitable for classroom implementation.
Acquire proficiency in using AI tools through learning and training.
Design activities that incorporate artificial intelligence, promoting collaborative work
and critical thinking.
Test the activities with students to ensure their effectiveness.
In conclusion, achieving a coherent micro-curricular planning and didactic sequence
requires teachers to be well-prepared in the subject matter and possess pedagogical knowl-
edge, didactic skills, and proficiency in virtual platforms, digital environments, and genera-
tive artificial intelligence tools [25].

3. Materials and Methods

This study employed a quantitative research design to investigate the impact of
generative artificial intelligence tools and the implementation of an instructional design
matrix on the construction of massive MOOC virtual classrooms.
The participants in this study were 42 teachers; the selection process involved purpo-
sive sampling to ensure representation from different disciplines and levels of teaching
experience. All participants had experience using generative artificial intelligence tools in
their educational practices.
In our study, we used a purposive sampling approach to select participants, ensuring
representation from different disciplines and levels of teaching experience. While we
recognize that a larger sample size could increase the generalizability of the results, it is
Sustainability 2023, 15, 11524 10 of 20

also essential to keep in mind the nature of our study, which focused on the specific context
of massive online classrooms (MOOCs).
MOOCs are online learning environments that allow the participation of large numbers
of students from diverse geographic locations and educational backgrounds. Since our
study focused on the impact of generative artificial intelligence tools and instructional
design matrices in this specific context, we felt that a sample of 42 teachers was adequate to
capture various relevant experiences and perspectives.
The course to which the methodology was applied had a total of 47 teachers, of which
42 responded; when using the formula with a known universe size, with a margin of error
of 5% and a confidence level of 95%, the sample of 42 teachers is within the allowed range.
A quantitative survey administered to participants was used for data collection. The
survey comprised a questionnaire on generative artificial intelligence tools, including
ChatGPT, Humata.ai, and other identified devices. Participants were also asked about
using the 4PADAFE instructional design matrix in their teaching practices. The survey was
administered online, and participants provided their responses anonymously. The survey
dataset is available in the Mendeley repository [27].
Several steps were taken to ensure the survey’s accuracy and validity. First, a peer
validation process was used, where experts reviewed the questionnaire and provided
comments and suggestions for improvement. Adjustments and revisions were made based
on these comments to ensure the questions were clear and relevant.
In addition, reliability measures were used to assess the internal consistency of the
items in the questionnaire. Cronbach’s alpha coefficient was applied in the SPSS application,
and Table 1 shows the reliability of the measurement scales used in the questionnaire. A
Cronbach’s alpha coefficient of 0.978 indicates a higher internal consistency of the questions
in each scale. The value obtained is very positive and suggests a high internal consistency of
the items used in the questionnaire. This value is indicative of the high internal consistency
of the items used in the questionnaire.

Table 1. Cronbach’s alpha coefficient was calculated with SPSS statistical analysis software.

Cronbach Alpha Based on

Cronbach’s Alpha N of Elements
Standardized Items
0.978 0.979 42

Regarding the participant selection process, purposive sampling was used to ensure
representation from different disciplines and levels of teaching experience. Forty-two
university professors with experience using generative artificial intelligence tools in their
educational practices were selected.
Quantitative data obtained from the surveys were analyzed using descriptive statistics.
The frequency and percentage of tool use were calculated, as well as the use of instructional
design matrices, to identify patterns and trends among participants.
It was found that a small percentage of teachers show total resistance to the use of
generative artificial intelligence tools and do not recognize their relevance in the learning
process. This may be due to a lack of opportunities to experiment with these tools and a
lack of alignment with current educational trends. It is critical to address these concerns
and resistance by providing training and support for teachers to understand the potential
benefits of these tools in education.
It is important to recognize certain limitations of this study. First, the sample size
was relatively small, consisting of teachers from a specific region and academic disciplines.
Generalization to larger populations may be limited. Second, the study focused on using
specific generative artificial intelligence tools and instructional design matrices, potentially
excluding other tools and methodologies. Finally, the study relied on self-reported data,
which may be subject to response bias.
 It is important to recognize certain limitations of this study. First, the sample size was
relatively small, consisting of teachers from a specific region and academic disciplines.
Generalization to larger populations may be limited. Second, the study focused on using
Sustainability 2023, 15, 11524 specific generative artificial intelligence tools and instructional design matrices, 11 poten-
of 20
tially excluding other tools and methodologies. Finally, the study relied on self-reported
data, which may be subject to response bias.
Despitethese
Despite theselimitations,
limitations, this
this study
study provides
providesvaluable
valuable insights
insights into
into the
theimpact
impactof of
generative artificial intelligence tools and instructional design matrices in the
generative artificial intelligence tools and instructional design matrices in the context ofcontext of
MOOCvirtual
MOOC virtualclassrooms.
classrooms. TheThefindings
findingscontribute
contribute to
tothe
theexisting
existingliterature
literatureand
andshould
should
inform future educational research and
inform future educational research and practice. practice.

4.4.Results
Results
The results
The results revealed that
thatgenerative
generativeartificial intelligence
artificial intelligencetools, such
tools, as ChatGPT,
such as ChatGPT, Hu-
mata.ai, and
Humata.ai, other
and otheridentified
identifiedtools, were
tools, werewidely
widely utilized byby
utilized the
theparticipating
participating teachers
teachers in
their
in theireducational
educationalpractices.
practices.Figure
Figure66presents
presentsthe
the AI
AI tools that teachers
teachers most
most frequently
frequently
utilized among
utilized among the the participants.
participants.ChatGPT
ChatGPT emerged
emerged as the most
as the popular
most tool, tool,
popular with with
a usagea
rate ofrate
usage 95.2%. Humata.ai
of 95.2%. followed
Humata.ai at 31%,atChat
followed 31%,PDF.com at 28.6%,
Chat PDF.com Studio.AI
at 28.6%, at 26.2%,
Studio.AI at
26.2%,
Leonardo Leonardo AI at 16.7%,
AI at 16.7%, Tome AI Tome
and AI and You.com
You.com at 14.3%ateach,
14.3% each,
and andFliki
finally, finally,
AI atFliki AI
11.9%.
atThese
11.9%. These statistics demonstrate educators’ widespread adoption and
statistics demonstrate educators’ widespread adoption and utilization of these gen-utilization of
these generative
erative AI tools. AI tools.

Figure6.6.Summary
Figure Summaryof
ofthe
thegenerative
generativeAI
AItools
toolsmost
mostfrequently
frequentlyutilized
utilizedby
byteachers.
teachers.

These
Thesetools
toolswere
wereprimarily
primarilyemployed
employedas asvirtual
virtualassistants,
assistants,providing
providingpersonalized
personalized
responses, answering student queries, and offering real-time support. Using
responses, answering student queries, and offering real-time support. Using generative generative ar-
tificial intelligence tools significantly contributed to student engagement and participation
artificial intelligence tools significantly contributed to student engagement and participa-
in virtual
tion classrooms.
in virtual classrooms.
Figure
Figure77shows
showsthat
thatafter
afterconducting
conductingthetheanalysis,
analysis,thethequestions
questions related
related to
toChatGPT,
ChatGPT,
4PADAFE,
4PADAFE,and andMOOC
MOOCthat received
that more
received morepositive responses
positive on the
responses on Likert scale,scale,
the Likert with with
val-
ues between 4 and 5 indicating “Agree” and “Strongly agree”, respectively,
values between 4 and 5 indicating “Agree” and “Strongly agree”, respectively, were were grouped.
These responses
grouped. Thesewere classified
responses wereaccording to according
classified the age of the participants,
to the age of theand it was observed
participants, and it
that individuals aged 47 to 57 responded most positively, followed
was observed that individuals aged 47 to 57 responded most positively, followed by those aged 36 by to 46
those
and then those aged 25 to 35. The responses reflect a positive perception of
aged 36 to 46 and then those aged 25 to 35. The responses reflect a positive perception ofhow artificial
intelligence tools enhance the educational experience. Additionally, it is considered that
ChatGPT and the 4PADAFE matrix can help teachers save time in preparing materials and
educational resources for MOOC courses. Lastly, it is believed that ChatGPT can facilitate
the exploration of complex topics in the classroom more easily for students.
 how artificial intelligence tools enhance the educational experience. Additionally, it is con-
how artificial intelligence tools enhance the educational experience. Additionally, it is con-
sidered that ChatGPT and the 4PADAFE matrix can help teachers save time in preparing
sidered that
materials andChatGPT and the
educational 4PADAFE
resources formatrix
MOOC cancourses.
help teachers save
Lastly, it time in preparing
is believed that
materials and educational resources for MOOC courses. Lastly, it is believed that
Sustainability 2023, 15, 11524 ChatGPT can facilitate the exploration of complex topics in the classroom more easily 12 offor
20
ChatGPT can facilitate the exploration of complex topics in the classroom more easily for
students.
students.

Figure 7. Summary of the survey responses of the 42 teachers.

Figure 7.
Figure 7. Summary
Summary of
of the
the survey
survey responses
responses of
of the
the 42
42 teachers.
teachers.
Figure 8 illustrates that teachers perceive generative artificial intelligence tools as ef-
Figure 8 illustrates
Figure illustrates that
thatteachers
teachers perceive
perceivegenerative
generative artificial intelligence tools as ef-
fectively enhancing students’ learning motivation. The dataartificial intelligence
reveal that tools
teachers between as
fectively enhancing
effectively enhancing students’
students’ learning
learning motivation.
motivation. The
The data
data reveal
reveal that teachers between
between
the ages of 25 and 35 generally hold positive views, rating between 3 and 5 on the Likert
the ages
the
scale;ages of 25
of 25 and
teachers and 35
aged 35 generally
36generally holdapositive
hold
to 46 exhibit positive views,
range ofviews,
opinions,rating
rating
with between
ratings33between
between and 55 on
and on2the
the Likert
andLikert
5 on
scale; teachers
scale; teachers aged
aged 36
36 to
to 46
46 exhibit
exhibit aa range
range ofof opinions,
opinions, with
with ratings
ratings between
between 22 and and 55 on
on
the Likert scale; and, although teachers aged 47 to 57 tend to be more negative, the major-
the Likert
the Likertscale;
scale;and,
and,although
althoughteachers
teachersaged
aged4747toto5757tend
tendtotobebemore
more negative,
negative, thethe major-
majority
ity still rate between 3 and 5 on the Likert scale, indicating a favorable perception overall.
ity still
still raterate between
between 3 and3 and
5 on5 the
on the Likert
Likert scale,
scale, indicating
indicating a favorable
a favorable perception
perception overall.
overall.

Figure 8.
Figure Generative artificial
8. Generative artificial intelligence
intelligence in
in conjunction
conjunction with
with educational experience.
Figure 8. Generative artificial intelligence in conjunction with educational experience.
Figure 9 shows that teachers across all age ranges, from 25 to 58, hold positive opinions
regarding the improvement of the educational experience through generative artificial
intelligence tools. The scale ratings range between 3 and 5, demonstrating a widespread
consensus among teachers regarding the positive impact of generative AI tools on the
educational experience.
 Figure 9 shows that teachers across all age ranges, from 25 to 58, hold positive opin-
ions regarding the improvement of the educational experience through generative artifi-
cial intelligence tools. The scale ratings range between 3 and 5, demonstrating a wide-
Sustainability 2023,spread
15, 11524consensus among teachers regarding the positive impact of generative AI tools on 13 of 20
the educational experience.

Figure 9. Generative artificial

Figure intelligence
9. Generative in conjunction
artificial with
intelligence student motivation.
in conjunction with student motivation.

Cluster AnalysisCluster Analysis

and Modeling and Modeling
Groups Groups
with Random with Random Forest
Forest
Cluster analysisCluster analysis
and group and group
modeling with modeling with have
random forest randombeenforest have
widely been
used in widely used in
data mining and machine learning. These techniques make
data mining and machine learning. These techniques make it possible to identify patterns it possible to identify patterns
and group similar observations
and group similar observations into distinct sets. into distinct sets.
Cluster analysis, Cluster analysis,
also known also knownisas
as clustering, clustering,
used to groupisobjects
used toorgroup
cases objects
based onor cases based on
their similarity in characteristics or attributes. In the case of Figure 10, three clearly defined
their similarity in characteristics or attributes. In the case of Figure 10, three clearly defined
clusters can be observed. These clusters represent questions related to different aspects
clusters can be observed. These clusters represent questions related to different aspects of
of the 4PADAFE methodology and the application of generative artificial intelligence
the 4PADAFE methodology and the application of generative artificial intelligence in ed-
in education.
ucation.
Analysis of the quantitative data obtained from the survey identified several key
Analysis of the quantitative data obtained from the survey identified several key
themes related to participants’ experiences and perspectives:
themes related to participants’ experiences and perspectives:
The participants highlighted the ability of these tools to personalize learning experi-
The participants highlighted the ability of these tools to personalize learning experi-
ences, adapt to individual student needs, and enhance student motivation and engagement.
ences, adapt to individual student needs, and enhance student motivation and engage-
The automation of content generation and the provision of real-time support were also
ment. The automation of content generation and the provision of real-time support were
recognized as significant benefits.
also recognized as significant benefits.
Some teachers expressed concerns about the reliability and accuracy of generative
Some teachers expressed
artificial concerns
intelligence tools,about the reliability
particularly in complexand subject
accuracy of generative
areas. Technical difficulties and
artificial intelligence tools, particularly
the learning in complex
curve associated subject areas.
with utilizing theseTechnical
tools weredifficulties and as challenges.
also identified
the learning curve associated with utilizing these tools were also identified as challenges.
Participants emphasized the value of the 4PADAFE instructional design matrix in
Participants emphasized
guiding the value ofofthe
the development 4PADAFE activities.
co-curricular instructionalThedesign matrix
structured in
approach provided
guiding the development of co-curricular activities. The structured approach provided
clarity and coherence, ensuring that activities aligned with the learning objectives and
clarity and coherence,
promoted ensuring that educational
an effective activities aligned with the learning objectives and
process.
promoted an effective educational
Integrating process. artificial intelligence tools and instructional design matrices
generative
Integratingsignificantly
generative contributed
artificial intelligence
to studenttools and instructional
engagement design matrices
and active participation. Teachers reported
significantly contributed to student engagement and active participation.
feeling more connected to the course materials and appreciated the Teachers
personalized support
and tailored learning experiences.
These findings highlight the positive impact of generative artificial intelligence tools
and instructional design matrices in constructing massive MOOC virtual classrooms. These
tools and methodologies enhanced student engagement, personalized learning experiences,
and improved educational outcomes.
Sustainability 2023, 15, 11524 14 of 20

Sustainability 2023, 15, 11524 14 of 20

reported feeling more connected to the course materials and appreciated the personalized
support and tailored learning experiences.

Figure
Figure 10. Summary
Summary of
of the
the most
most used
used generative
generative AI
AI tools.
tools.

Figurefindings
These 11 summarizes thethe
highlight toppositive
ten questions,
impactwhere red indicates
of generative strong
artificial disagreement,
intelligence tools
Sustainability 2023, 15, 11524 orange indicates disagreement, neutral green indicates agreement, 15 of 20
and instructional design matrices in constructing massive MOOC light blue
virtual indicates
classrooms.
agreement,
These tools and electric blue indicates
methodologies strong
enhanced agreement.
student engagement, personalized learning ex-
periences, and improved educational outcomes.
Figure 11 summarizes the top ten questions, where red indicates strong disagree-
ment, orange indicates disagreement, neutral green indicates agreement, light blue indi-
cates agreement, and electric blue indicates strong agreement.
The study outcomes provide valuable insights for educators and institutions seeking
to leverage technology and instructional design strategies to optimize online teaching and
learning environments.
Teachers can use generative artificial intelligence tools to improve student engage-
ment and motivation by providing relevant content tailored to their preferences and learn-
ing styles. In addition, implementing an instructional design matrix ensures consistency
and quality in the design of activities by aligning them with educational objectives and
providing a clear structure for their implementation.

Figure
Figure 11.
11. Summary
Summary of
of the
the top
top ten
ten questions.
questions.

5. Discussion
The study outcomes provide valuable insights for educators and institutions seeking
to leverage technologypart
The quantitative and of
instructional design
the research strategies
consists to optimize
of applying online teaching
the instructional and
design
learning
matrix ofenvironments.
the 4PADAFE methodology in a virtual course called “Generative Artificial In-
telligence Tools for Education: ChatGPT Techniques” The population included forty-two
(42) teachers who participated in the course. A questionnaire of 45 questions was applied
through Google Forms to be answered voluntarily.
The respondents were 50% women and 50% men; the teachers belonged to the dif-
Sustainability 2023, 15, 11524 15 of 20

5. Discussion
The quantitative part of the research consists of applying the instructional design
matrix of the 4PADAFE methodology in a virtual course called “Generative Artificial
Intelligence Tools for Education: ChatGPT Techniques” The population included forty-two
(42) teachers who participated in the course. A questionnaire of 45 questions was applied
through Google Forms to be answered voluntarily.
The respondents were 50% women and 50% men; the teachers belonged to the differ-
ent modalities of study, where the most significant number corresponded to the face-to-face
modality with 59.5%, followed by the hybrid modality with 23.8%, the virtual modality
with 14.3%, and the distance modality with 2.4%. Generative artificial intelligence tools and
implementing an instructional design matrix are crucial in constructing massive virtual
MOOC classrooms. These technological educational trends offer new opportunities to per-
sonalize learning, adapt to individual learner needs, and improve the quality of educational
content. Generative artificial intelligence enables the automated creation of personalized
educational materials, while the instructional design matrix provides structured guidance
for designing and planning micro-curricular activities.
These technological trends become even more relevant in massive MOOC virtual
classrooms aiming to reach many students. Generative artificial intelligence and the
instructional design matrix make it possible to efficiently manage and organize educational
content, adapting it to the needs of a diverse audience.
Teachers can use generative artificial intelligence tools to improve student engagement
and motivation by providing relevant content tailored to their preferences and learning
styles. In addition, implementing an instructional design matrix ensures consistency
and quality in the design of activities by aligning them with educational objectives and
providing a clear structure for their implementation.
Teachers value the potential support of these AI tools in the learning process, such as
brainstorming and analysis. However, they also expressed concerns about privacy, ethics,
and intellectual property.
Adopting generative artificial intelligence tools in education faces specific challenges,
as a small percentage of teachers show total resistance to their use and do not recognize their
relevance in the learning process. This resistance may be due to the lack of opportunities to
experiment with these tools and the lack of alignment with current educational trends. To
achieve successful implementation, it is critical to address the concerns and resistance of
these teachers by providing them with the training and support necessary to understand
the potential benefits of generative artificial intelligence tools in education.
The data analysis revealed that generative artificial intelligence tools and the instruc-
tional design matrix are powerful resources to improve the quality and effectiveness of
MOOCs’ massive virtual classrooms. These technological educational trends offer inno-
vative opportunities to personalize learning [28], adapt to students’ individual needs,
and promote a more inclusive and accessible education. As we continue to explore and
harness the potential of these tools, we can move toward a more inclusive and accessible
education [29,30].
In addition, using generative artificial intelligence tools in education promotes inclu-
sion and equity by improving access to information for students with different abilities and
learning styles. The personalization of educational content and activities allows us to meet
the specific needs of each student, facilitating their academic progress and comprehensive
development.
Integrating virtual learning assistants based on generative artificial intelligence is also
an effective strategy to enhance students’ critical thinking and creativity. These assistants
provide individualized support, stimulate problem-solving, and promote the exploration
of new ideas, which contributes to the development of higher cognitive skills.
On the other hand, offering integrative curricula based on generative artificial intelli-
gence tools provides students with a broader and multidimensional vision of knowledge,
Sustainability 2023, 15, 11524 16 of 20

fostering an understanding of interdisciplinary relationships and preparing them to face

the challenges of the real world.
Using generative artificial intelligence tools in education has opened up new pos-
sibilities and opportunities to improve teaching and learning processes. However, it is
essential to recognize and discuss the inherent limitations of these tools, including po-
tential ethical issues, biases, and other limitations that may arise in their application in
educational settings.
These tools collect and analyze large amounts of data, raising concerns about student
information privacy and security. In addition, there is the potential for these tools to collect
sensitive or personal data without proper consent, raising questions about compliance with
data protection regulations and ethics in handling student information.
These tools are trained using existing data sets, which may contain inherent biases,
such as gender, race, or social class biases. This can lead to biased or discriminatory
results in generating content or the answers provided by the tools. Addressing these biases
and ensuring fairness and impartiality in using generative artificial intelligence tools in
education is critical.
In addition, technical limitations are also a factor to consider; although generative
artificial intelligence tools have made significant progress in generating coherent and
relevant content, they can still present difficulties in understanding complex contexts or
interpreting abstract concepts. This can affect the quality and accuracy of the answers
generated, which requires teacher supervision and correction to ensure the accuracy of the
information provided to students.
Another aspect to consider is the over-reliance on generative artificial intelligence
tools. While these tools can help provide additional support and facilitate the teaching
process, there is a risk that students may become overly dependent on them and fail to
develop critical skills, such as problem-solving or critical thinking. Balancing these tools
with pedagogical approaches that foster students’ holistic development is essential.
Applying generative artificial intelligence tools and implementing the 4PADAFE in-
structional design matrix is crucial for building massive virtual MOOC classrooms. These
educational technology trends offer new opportunities to personalize learning, adapt to
individual learner needs, and improve the quality of educational content. Generative
artificial intelligence tools enable the automated creation of personalized educational ma-
terials, while the instructional design matrix provides structured guidance for designing
and planning micro-curricular activities. These technology trends are especially relevant in
massive MOOC virtual classrooms that seek to reach many students. Generative artificial
intelligence and the instructional design matrix make it possible to efficiently manage and
organize educational content, tailoring it to the needs of a diverse audience. Teachers can
use generative artificial intelligence tools to improve student engagement and motivation
by providing relevant content tailored to their preferences and learning styles. Implement-
ing the instructional design matrix ensures consistency and quality in the design of the
activities, aligning them with the educational objectives and providing a clear structure
for performance.
One limitation of our study is the relatively small sample size of 42 teachers. This
may limit the generalizability of the findings to larger populations. Future research could
consider expanding the sample size to include a more diverse range of participants from
different regions and academic disciplines to enhance the generalizability of the results.
Another limitation is the focus on specific generative artificial intelligence tools and
instructional design matrices. Other tools and methodologies not included in our study
may be available. Future research could explore a broader range of tools and methodologies
to provide a more comprehensive understanding of their impact in virtual classrooms.
To gain deeper insights into teachers’ experiences and perceptions, future research
could incorporate qualitative data such as interviews or open-ended survey questions. This
would provide a richer understanding of how teachers incorporate generative AI tools
Sustainability 2023, 15, 11524 17 of 20

in their teaching methods, specific tasks or interactions facilitated by these tools, and any
observed benefits or challenges.
Conducting longitudinal studies over an extended period could provide a more
comprehensive assessment of the long-term effects and sustainability of using generative
AI tools and instructional design matrices in virtual classrooms.
Including comparison groups of teachers who do not use generative AI tools or
instructional design matrices would allow for a more rigorous evaluation of the effective-
ness and impact of these tools on student engagement, learning outcomes, and overall
educational experience.
By addressing these limitations and exploring additional avenues for research, we can
further refine our understanding of the potential of generative AI tools and instructional
design matrices in education and identify strategies to optimize their implementation
and effectiveness.
Finally, generative artificial intelligence tools facilitate the initial development of
ideas and reflection on them, favoring the generation of creative and innovative solutions.
Similarly, automated assessment and other assessment innovations enable more accurate
and objective tracking of student progress, provide immediate feedback, and support
data-driven decision-making by teachers.

6. Practical Implications
The results of this study have important practical implications for implementing
generative artificial intelligence tools and instructional design matrices in virtual classrooms.
The following are some specific recommendations for educators and institutions interested
in adopting these technologies:
Training and professional development: Providing educators with adequate training
in using generative artificial intelligence tools and applying instructional design matrices is
critical. This study includes opportunities to acquire the technical skills and pedagogical
knowledge necessary to use these tools effectively in the educational environment. Educa-
tors must understand how to take full advantage of the capabilities of generative AI tools
and how to integrate them into their teaching practices effectively.
Personalization and customization: The results of this study highlight the ability of
generative AI tools to personalize learning and adapt to the individual needs of learn-
ers. Educators should explore leveraging these capabilities to deliver more relevant and
meaningful learning experiences.
This involves using generative AI tools to provide personalized content and feedback
and to adapt teaching strategies to students’ learning styles and preferences.
Curriculum design and planning: Implementing instructional design matrices can
be an effective strategy to ensure consistency and quality in the design of activities in
virtual classrooms. Educators should use these matrices as structured guides to develop
activities aligned with the learning objectives, curricular content, and teaching strategies.
This will help ensure that the activities are relevant and practical and promote an effective
educational process.
Evaluation and feedback: Generative AI tools can be necessary for student assessment
and feedback. Educators can use these tools to automate task assessment and provide
immediate feedback to students. However, educators must monitor and verify the accuracy
and reliability of AI-generated assessments. In addition, it is crucial to combine automated
assessment with personalized, human feedback to provide students with a complete picture
of their progress and areas for improvement.
Ethical and privacy considerations: Educators should be aware of ethical and privacy
issues when adopting generative AI tools. Respecting student rights and privacy and ad-
dressing potential bias in AI-generated results is essential. Educators must understand and
comply with policies and regulations related to student data use and ensure information
security and confidentiality.
Sustainability 2023, 15, 11524 18 of 20

Continuous evaluation and improvement: Ongoing evaluation of the impact and

effectiveness of generative AI tools and instructional design matrices is critical. Educators
and institutions should collect student feedback and analyze data to understand how these
technologies influence teaching and learning. This feedback and analysis can help identify
areas for improvement, adjust teaching practices, and optimize the implementation of
generative AI tools.

7. Conclusions
This work is relevant for educators, educational institutions, and practitioners inter-
ested in harnessing the potential of generative artificial intelligence tools and the 4PADAFE
instructional design matrix for educational purposes. It is also valuable for researchers
and academics seeking to explore the impact of these technologies in educational settings.
The contribution of this work lies in providing a comprehensive and practical understand-
ing of how generative artificial intelligence tools and instructional design matrices can
optimize virtual classrooms and enhance the learning experience. The study presents
original research on the impact of these technologies in constructing large-scale virtual
classrooms and offers valuable insights into their implementation, benefits, and challenges.
Users can enjoy several advantages by leveraging generative artificial intelligence tools and
instructional design matrices. These include increased personalization of learning, tailored
to individual learner needs, enhanced engagement and motivation, automated generation
of instructional content, improved efficiency in content management and organization, con-
sistent activity planning, and a clear structure for designing and developing instructional
activities. Ultimately, this approach can significantly enhance the quality of the educational
process and foster better learning outcomes in virtual environments.
This report presented general findings without specific details regarding the frequency
of use, specific tasks, or interactions facilitated by ChatGPT. In future research, we will
incorporate additional questions to address this limitation and better understand how
teachers incorporate these tools into their teaching methods. Addressing privacy, ethics, and
intellectual property concerns associated with using generative artificial intelligence tools
in education is crucial. Further research and experimentation are necessary to maximize
the benefits of generative artificial intelligence tools in education. Exploring ways to
enhance inclusion and equity through these tools is essential, ensuring equitable access to
information and tailoring educational content and activities to meet students’ needs.
Moreover, we can develop additional strategies to integrate generative-artificial-
intelligence-based virtual learning assistants, promoting students’ critical thinking skills
and creativity. Exploring more integrative approaches to curriculum design based on
generative artificial intelligence tools can foster a multidimensional view of knowledge and
prepare students for real-world challenges. It is crucial to continue researching and devel-
oping innovations in automated assessment and other assessment techniques that enable
accurate and objective tracking of student progress, providing immediate feedback, and
supporting data-driven decision-making by teachers. In future research, we will consider
incorporating qualitative data to obtain a more complete and holistic understanding of the
effects and implementation of generative artificial intelligence tools in education.

Author Contributions: Conceptualization, L.I.R.-R., P.A.-V., J.D.-M.-L. and M.G.-R.; methodology,

L.I.R.-R. and P.A.-V.; validation, L.I.R.-R. and P.A.-V.; formal analysis, L.I.R.-R., P.A.-V. and M.G.-R.;
investigation, L.I.R.-R., P.A.-V., J.D.-M.-L. and M.G.-R.; resources, P.A.-V. writing—original draft
preparation, L.I.R.-R., P.A.-V., J.D.-M.-L. and M.G.-R.; writing—review and editing, L.I.R.-R., P.A.-V.,
J.D.-M.-L. and M.G.-R.; supervision, P.A.-V. and M.G.-R.; project administration, P.A.-V.; funding
acquisition, P.A.-V. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Universidad de Las Américas-Ecuador, as part of the
internal research project INI.PAV.22.01, INI.PAV.22.02 and project INI.PAV.23.01.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Sustainability 2023, 15, 11524 19 of 20

Data Availability Statement: https://doi.org/10.17632/2kyksx8hty.1 (accessed on 23 June 2023).

Conflicts of Interest: The authors declare no conflict of interest.

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