Do the scientific community and society agree
on the use of Artificial Intelligence in education?
¿Coinciden la comunidad científica y la
sociedad sobre el uso de la Inteligencia Artificial en educación?
Dra. Sonia
Martín-Gómez. Profesora adjunta. Universidad San Pablo
CEU. Madrid. España
Dr. Ángel Bartolomé
Muñoz de Luna. Profesor Titular. Vicerrector de Estudiantes
y Vida Universitaria. Universidad San Pablo CEU. Madrid. España
Received: 2024/06/04 Reviewed 2024/07/28 Accepted: :2024/12/10 Online First: 2024/12/18 Published: 2025/01/07
Cómo citar este artículo:
Martín-Gómez,
S., & Muñoz de Luna, Ángel B. (2025). ¿Coinciden la comunidad científica y
la sociedad sobre el uso de la Inteligencia Artificial en educación? [Do the scientific
community and society agree on the use of Artificial Intelligence in
education?]. Pixel-Bit. Revista De Medios Y Educación, 72, 139–157. https://doi.org/10.12795/pixelbit.107530
The main objective
of this research is to explore and understand the development and
implementation of AI in the context of higher education at a scientific and
social level, using a systematic methodology of reviewing scientific papers
from the Web Of Science (WOS) database for the
scientific part and a social listening analysis for the social field. The
bibliometric study, through the Rstudio Cloud
application, allows us to extract a meta-analysis on the topic of IA in higher
education, from 2019 to the present, achieving an evaluation of 32 articles
according to the guidelines of the PRISMA declaration.
For its part, the Brandwatch platform allows us to find out what is being
said online about the use of AI in higher education, studying a total of 27,735
mentions, only from the last year.
By comparing the
scientific and social results, conclusions are drawn on the current challenges
of AI in universities, highlighting the need for researchers to start analysing the impact of the good use of AI tools as a
teaching methodology, so that society can also highlight it in its mentions on
the networks.
El objetivo principal de esta
investigación es explorar y comprender el desarrollo y la implementación de la Inteligencia
Artificial (IA) en el contexto de la educación universitaria a nivel científico
y a nivel social. Se va a usar una metodología sistemática de revisión de
documentos científicos a partir de la base de datos Web Of Science (WOS), para
la parte científica y un análisis de social listening para el ámbito social. El
estudio bibliométrico, a través de la aplicación Rstudio Cloud, permite extraer
un metaanálisis sobre el topic IA en educación superior, desde el año 2019
hasta la actualidad, consiguiendo, según directrices de la declaración PRISMA,
una valoración de 32 artículos.
Por su parte, la plataforma
Brandwatch permite conocer lo que se habla en la red sobre el uso de IA en la
educación superior, estudiando un total de 27.735 menciones, solo del último
año. Comparando los resultados científicos y sociales, se alcanzan conclusiones
sobre los desafíos actuales de la IA en la universidad, destacando que es
necesario que los investigadores empiecen a analizar los efectos del buen uso
de las herramientas de la IA como metodología docente, de forma que la sociedad
pueda destacarlo también en sus menciones en redes.
KEYWORDS · PALABRAS CLAVES
Artificial intelligence; higher education;
scientometrics; mentions; social listening
Inteligencia artificial;
educación superior; cienciometría; menciones; escucha social
1. Introduction
The use of Artificial Intelligence (AI) has been increasing
in recent years, making a name for itself in various fields such as medicine,
finance, law, law enforcement, business, and industry and entertainment
(Salas-Pilco & Yang, 2022); therefore, the IoT (Internet of Things) is a
thing or a collective network of connected devices that facilitate
communication between connected devices, devices and the cloud, as well as
between the devices themselves) will continue to grow in the coming years to
reach 66 billion units in 2026, with 87% of the total number of units in the
next few years to reach 66 billion units in 2026. Users state that, once they
have tried such devices, they will no longer give up their benefits, according
to the second edition of the Things Matter 2019 Report, produced by Telefónica.
The technological evolution of the last few years has
had a positive and/or negative impact on societies around the world so that
people's modus vivendi are affected. Health, the economy, and, obviously, in
education and training (Alonso-de-Castro & García-Peñalvo, 2022). This means that AI has become a synonym for
new promises, but it is also necessary to take into account the risks that come
with the massification of digital technologies in the different spheres of
economic and social life and social problems of the 21st century, as there is a
perception that it will put jobs at risk of those who do not adapt to this new
technological revolution.
Many studies attempt to gauge the pace and depth of
the changes that are taking place as many industries are automating processes
thanks to new technologies and new innovations. Machines are available and
prototypes of inventions are tested that until recently seemed to be science
fiction (Kaku, 2012). In the face of all these developments, we wonder how AI
will impact education, which is considered a fundamental pillar for society's
progress and individuals' development. In an increasingly digitised and globalised
world, AI has become an increasingly important tool and essential tool for
enhancing and personalising the educational experience, understanding the
ability of machines to learn, reason, and autonomously make decisions, and its
application in education is constantly growing and adapting(Halili, 2019).
Artificial intelligence (AI) is playing an
increasingly important role in the field and the educational environment is
affected by all the changes it generates, ranging from preschool stages to
higher or post-graduate levels (Moreno & Pedreño, 2020), as their
application has the potential to transform how we teach and learn.
Here are some of the ways in which AI is being used in
education:
1. Personalisation of
learning. AI can adapt the content and pace of learning to each learner's
individual needs. This means that students can receive specific instruction and
exercises according to their level of proficiency, learning ability, and
learning style, which can increase the effectiveness of learning.
2. Virtual tutoring. AI
systems can act as virtual tutors, providing instant feedback to students as
they work on problems or tasks. This can help learners to understand the
concepts and correct errors better immediately.
3. Data collection and
analysis. AI can collect and analyse large amounts of data on student
performance. Educators can use this information to identify areas for
improvement, to identify trends in learning and to make informed decisions
about teaching.
4. Automation of
administrative tasks. AI helps automate tasks administrative tasks, such as
grade management, class scheduling, and communication with students and
parents. This allows educators to focus more on individualised teaching and
support.
5. Adaptive learning. AI
systems help to adjust the content and the learning activities according to the
progress of each student. This can ensure that students are constantly
challenged and engaged.
6. Evaluation of
open-ended responses. AI allows for the evaluation of open-ended responses,
such as tests and answers to developmental questions, using algorithms of
natural language processing. This can save educators time in correction and
provide more objective feedback.
7. Access to online
educational resources.AI helps students find online learning resources that are
tailored to their specific needs, recommending relevant courses, tutorials and
study material.
In conclusion, we can affirm that specifically in
education, the artificial intelligence education refers to its use in support
of feedback and guidance and automated systems in the field of education (Song
& Wang (2020). In this scenario, the teacher has to be the protagonist in
the classroom, analysing the information provided by the AI, and guiding and
articulating the work of the students. The challenge relates more to the
capacities of teachers to carry out these tasks, as relatively few have the
necessary skills to perform these tasks to process the sheer volume of
individual information that the new systems provide, and/or to translate it
into the personalised responses they are supposed to provide (Lu& Harris,
2018).
In addition, it is important to remember that the
successful implementation of AI in education also raises challenges and ethical
issues. These include concerns about the privacy of student data, equity in
access to technology, and the need to maintain a balance between automation and
human interaction in the educational process. AI in education is a powerful
tool, but its use needs to be carefully considered and monitored to ensure that
it benefits all stakeholders and students fairly and effectively.
Based on the above, this study aims, on the one hand,
to conduct an empirical analysis of the evidence found within the scientific
literature on the use of AI in education and, on the other hand, a social
listening analysis for the use of AI in education to see whether scientists and
society are moving in the same direction. There are some previous systematic
reviews on AI in education (Martinez-Comesaña et al., 2023; Jimbo-Santana et
al., 2023; Fajardo Aguilar, 2023), although they are very limited in comparison
to AI research, but there are no studies that have been conducted that compare
the opinion that emerges from (scientific) systematic reviews with the social
listening (society).
For Zawacki-Richter et al (2019) there is a lack of critical
reflection on the challenges and risks of AI in education in the majority of
scientific articles and a weak connection between AI and education with
theoretical pedagogical perspectives, so the need arises to follow up on the
exploring ethical and educational approaches in the application of their use in
higher education; Along the same lines, Hinojo-Lucena et al. (2019), after
carrying out a bibliometric study stressed that more empirical, research-based
results are needed in order to understand the potential of AI in higher
education.
In summary, this study attempts to address the
following research questions:
·
How do you approach the use of artificial intelligence
for university education?
·
Do scientists and society agree in their assessments
and has there been the same growth as in the past in both communities?
2. Methodology
For scientometrics or bibliometrics, this study follows the
guidelines of the Declaration of the European Parliament and the Council of
Europe, PRISMA, which consists of the use of search engines to search for
indexed articles in order to obtain the necessary
information required on studies that have already been carried out (Barquero
Morales, 2022; Page et al., 2021). The five-step framework of Arksey and
O'Malley (2005) for mapping the scientific literature,
consists of: a) identification of the research question; b) systematised
search for scientific evidence; c) selection of the most appropriate (d) data
extraction; and (e) data collection, summarisation
and dissemination of results.
The
study focuses on the scientific articles published in the Wos database in the
period 2019 to 2023, which have been processed using the Bibliometrix
application for R Studio Cloud, which allows a complete bibliometric analysis
to be performed, following the flow of scientific mapping work (Aria &
Cuccurullo, 2017). The articles obtained were selected based on a Boolean
search generalist "Artificial Intelligence AND university studies",
following these criteria of exclusion:
·
Type of document: article.
·
Years
of publication: between 2019 and 2023.
·
Language: English and Spanish
·
Category
of Wos: Education &Educational Research.
·
Web of Science Index: ESCI, SSCI and
ESCI-Expanded.
With
these restrictions, a total of 36 articles were obtained, which after reading
and the number of projects evaluated following PRISMA has been reduced to 32,
either because they are repeated or because their field of research is not
directly related to education.
For
research based on social listening, the methodology is used as a means of to
understand users' perceptions of a given topic or issue (Herrera et al., 2022),
as it works not only with one's own perception but also with any other
perception (Herrera et al., 2022) anchor point to be established between the
user and the subject under study, based on primarily in the use of technology
and algorithms that track and compile automatically gather data from various
online sources: social networks, blogs, forums, news, etc. and other types of
websites. Once the data is collected, it is then analysed
in order to identify patterns, tendencies and
feelings, applying techniques such as processing natural language (NLP) and
text analysis (Cambria, 2016).
In
general, the social media analysis process is usually divided into four phases:
(Stieglitz et al. 2018):
·
Discovery:
identification of content and corresponding keywords,
·
Hashtags,
etc. that will contribute to the definition of the objectives of the analysis
and the main hypotheses to be tested.
·
Monitoring:
identification of data sources and data collection.
·
Preparation:
preparing the data for further analysis.
·
Analysis:
application of various analysis methods and techniques to the data set prepared
to answer the questions posed in the discovery phase.
In
this research, as shown in Figure 1, the same steps will be followed as
proposed by Stieglitz, adding one more, which refers to the implementation of
the need for effective communication of the results of the project, including
the need of social network analysis, as proposed by
the Brandwatch software, which is the platform used
to carry out this social listening.
Figure 1
Mapping Brandwatch
core functions to the network analysis process framework
Brandwatch Search, a search engine, is used for the discovery
stage based on artificial intelligence, which uses sophisticated computer
processing techniques to process natural language. In this case, the search is
linked to the use of social networks in research. In the follow-up phase, the
so-called Query is formed which refers to the set of words used to retrieve
information from the platform's systems. For this purpose, Barean
operators have been used to combine the concepts and refine the results to be
achieved, as shown below:
This
query returns 2,150 mentions on the day of the survey alone in the last 30
days, having filtered by language (Spanish) but searching in anywhere in the
world. Therefore, tools are needed to segment and filter this information,
among others, a test preview to instantly assess the kind of mentions that are
retrieved from the current consultation logic, favouring
the intended social analysis; in this search, it has been decided to remove
websites that mentioned the terms consulted, but are not related to the
objective of the study.
Finally,
the query is maintained, filtered by language, invalid sites are eliminated and
a date range of one year is used to analyse whether
the evolution of the content object of study follows a certain pattern.
In
the last two stages, the results are analysed and
implemented through the use of so-called dashboards
which monitor and examine visually the key indicators.
For
this network analysis, a sampling rate of 100 % is used with sampling rates of
estimated mentions of 1,995 per month.
3. Analysis and
results
3.1. Statistical results of Bibliometrix for R Studio Cloud
Following the bibliometric study carried out with the
R Studio Cloud programme, we proceed to analyse the results obtained at the
scientific level in order to be able to respond to the questions of research.
3.1.1. Dataset
Figure 2 shows the annual
scientific production and highlights the high
interest the scientific awakening of AI in education in the years 21 and 22
there appears to be a decline in the scientific literature on this subject, we
will have to wait for the end of the year in order to
have real data on the publications produced.
Figure 2
Annual scientific production
Figure 3 shows the so-called three-field
graph (Sankey diagram), in this case, country, author and abstract and their
interactions with each other. The graph highlights visually the main transfers
between countries, actors and concepts which have in
the summaries. The width of the arrows in the chart is proportional to the
amount of flow.
Figure 3
Sankey diagram
It can be seen that Colombia
and Cuba are the countries where there is the highest production of scientific
use of terms related in the abstracts themselves to AI concepts, such as data,
artificial, intelligence, or learning and are the countries that bring together
the leading researchers, although in Spain and Peru, there are also some
researchers in this field type of issues.
3.1.2. Sources
Concerning the dispersion of
scientific literature, Bradford's Law confirms that if scientific journals are
ordered in a decreasing sequence of productivity from articles on a specific
field, these can be divided into a core of journals that The Bradford core area
(Bradfordcore-zone 1) and various clusters or zones (zones 2 and 3) containing approximately the same number
of items as the kernel, where the number of journals in the core and successive
zones is in a ratio relationship of 1: n: n2, as shown in Table 1.
Table 1
Bradford Core
|
MAGAZINE |
Ranking |
Freq |
Freq Acum |
Zone |
|
Tecnura |
1 |
5 |
5 |
Zone 1 |
|
Revista cubana de ciencias informáticas |
2 |
4 |
9 |
Zone 1 |
|
Formación universitaria |
3 |
2 |
11 |
Zone 1 |
|
Revista universidad y sociedad |
4 |
2 |
13 |
Zone 2 |
|
Academo (asunción) |
5 |
1 |
14 |
Zone 2 |
|
Actualidades investigativas en educación |
6 |
1 |
15 |
Zone 2 |
|
Diseases of the colon & rectum |
7 |
1 |
16 |
Zone 2 |
|
Educación |
8 |
1 |
17 |
Zone 2 |
|
Fem: revista de la fundación educación
médica |
9 |
1 |
18 |
Zone 2 |
|
Horizontes revista de investigación en
ciencias de la educación |
10 |
1 |
19 |
Zone 2 |
|
Información tecnológica |
11 |
1 |
20 |
Zone 2 |
|
Ingeniería electrónica, automática y
comunicaciones |
12 |
1 |
21 |
Zone 2 |
|
Ingeniería industrial |
13 |
1 |
22 |
Zone 2 |
|
Ingeniería y desarrollo |
14 |
1 |
23 |
Zone 2 |
|
Inter disciplina |
15 |
1 |
24 |
Zone 3 |
|
International Journal
of Morphology |
16 |
1 |
25 |
Zone 3 |
|
Propósitos y representaciones |
17 |
1 |
26 |
Zone 3 |
|
Revista cientifica |
18 |
1 |
27 |
Zone 3 |
|
Revista cubana de educación superior |
19 |
1 |
28 |
Zone 3 |
|
Revista cubana de informática médica |
20 |
1 |
29 |
Zone 3 |
|
Revista digital de investigación en
docencia universitaria |
21 |
1 |
30 |
Zone 3 |
|
Revista iberoamericana de tecnología en
educación y educación en tecnología |
22 |
1 |
31 |
Zone 3 |
|
Revista panamericana de salud pública |
23 |
1 |
32 |
Zone 3 |
|
Revista peruana de ginecología y
obstetricia |
24 |
1 |
33 |
Zone 3 |
According to this law (Figure
4), it is observed that such a dispersion does not exist, since almost all of
the publication frequency is grouped into three journals (those of the Bradford
core): Tecnura, Revista
Cubana de Ciencia, and Revista de Formación Universitaria, all Latin American, This
shows that scientific production on AI has its origins in South America.
Figure 4
Bradford's Law
3.1.3. Authors
Figure 5 demonstrates that
scientific production basically starts at the beginning of 2019, but it has
only one author: Alex Valenzuela-Fernandez, the one who accounts for most of
this annual production.
As far as personal
productivity is concerned, Lotka's law is not verified in this case (Fig. (6),
which states that a small number of authors publish a significant amount of
documents, i.e. it states a quantitative relationship between the authors and
the documents, and contributions produced in a given field over a given period
of time, since in a given field the in this case, there are many authors (a
total of 102 authors) who only sign two articles, scientific productivity is
therefore low.
Figure 5
Scientific production of the authors in recent years
Figura 6
Lotka's Law
3.1.4.
Documents
In the analysis concerning
documents, figure 7 shows the most frequent words used by the authors in this
case in the abstracts, with artificial and intelligence being the most
important most commonly used, along with university,
results and students, although in a smaller proportion lower.
Figure 7
Most relevant terms
Similar results can be seen in
the figure for the most common word cloud (Figure 8) which is also considered
to be a good formula for identifying the research topics of a scientific domain
(Li et al, 2021), in this case focusing on the 50 keywords, which include terms
extracted from abstracts and in Treemap (Figure 9),
which arranges the data hierarchically and has the structure of a tree in where
the data is organised in nested rectangles (one
inside the other). The size of the rectangle corresponds to the value of the
category or subcategory.
Figure 8
Word cloud
In the word cloud,
"artificial" (39 times), "intelligence" (38 times) stand
out, "University" (29 times) and others such as "results"
or "students" are also important, although they have fewer
repetitions. It is relevant to note that the three most common words are the
variables investigated in this study.
Figure 9
Treemap
3.2. Bibliometrix
Structural Analysis for R Studio Cloud
3.2.1. Conceptual structure
Figure 10 shows a word
co-occurrence matrix, taking into account that two words
co-occur when they appear simultaneously in the same document; two words
co-occur when they appear simultaneously in the same document, and when two
words co-occur when they appear simultaneously in the same document, words will
be more closely linked or associated with each other the greater the
co-occurrence between the words.
Therefore, the size of the
link between two words in a network will be proportional to theco-occurrence of these two words in the set of
documents to be taken as the basis for the sample. In this case, three
co-occurrence groups emerge, which are represented by three different colours in three clusters.
Figure 10
Cooccurrence of words
Figure 10 shows clusters of colours representing words that also use other authors
within the same cluster. For example, in the green cluster, it is observed that
the most commonly used terms are intelligence,
artificial and university and other authors also use them. This is repeated in
the other clusters, indicating patterns which reflect trends and concepts of
research interest.
3.2.2.
Social structure
Figure 11 is based on the
collaborative network or co-signing of publications, in this case between
authors. It shows that there is very little collaboration between them. The
fact that they form small collaborative sub-groups is not conducive to research
either.
Figure 11
Collaboration network
3.3. Results obtained from the
Brandwatch social listening software
In order to carry out this part of the research, 13,107 authors
were analysed and one total of 27,735 mentions in
networks.
As for the sources of content,
Figure 12 shows the total number of mentions from September 22 to 23, showing
that the highest volume of content (interactions) in May on the Twitter
network, which can be justified by the because on 25 May 2023, UNESCO mobilised Ministers of Education from all over the world
for a coordinated response to ChatGPT, in response to the rapid emergence of
powerful new generative AI tools to explore the opportunities, immediate and
far-reaching challenges and risks that AI applications pose to the education
systems.
Figure 12
Sources of content
In terms of the sentiment that
AI generates in society, understood as the number of total mentions over time
broken down by sentiment, figure 13 shows how there are many oscillations at
certain times of the year, but the tone is predominantly neutral in most of the
mentions, not standing out in any of them, nor did it have any strong feelings
of positive or negative, perhaps because society has not tested AI and cannot
value.
Figure 13
Sentiment over time
The topic wheel, in Figure 14,
analyses frequently used words and phrases in networks, allowing one to easily
see how the main themes (the inner ring) are related to the sub-themes (the
outer ring), highlighting how the AI relates to the sub-themes (the outer
ring), and highlighting how the AI relates in mentions made with students,
teachers and Chat Gpt, and something similar happens
to Chat Gpt which is related to university. In any
case, terms do not arise studied by the scientific community as AI and Chat GPT
tools, and therefore it is noted that the concern in networks and in the
scientific community about the use of AI in education goes in different
directions.
Figure 14
Topic Roundtable
Figure 15 shows the cloud of
words, phrases and entities that are found commonly in the mentions of the
selected time period, highlighting tools, technology,
data, information and people, among others, here too there is no coinciding
with the word cloud derived from scientific studies.
Figure 15
Word cloud
In terms of words and phrases
commonly found in the mentions of the selected time period, delineated
according to whether they are trending or losing the most important of these
are shown in Figure 16, where we can see that all of them are in the zone trending
topic, i.e. AI is trending in the networks but for objectives such as tools or Gpt Chat.
Figure 16
The trend of the themes
4. Discussion and
Conclusions
The incorporation of AI in university studies
generates a broad debate between teachers-researchers, students
and society in general.
Thus, it has been found that
at the scientific level, authors have addressed this issue from different
perspectives, discussing both the opportunities offered by AI and the
challenges it offers ethical and social concerns associated with its
implementation. The use of AI should be treated as a method of teaching
innovation that can generate benefits for students and teachers. Students, in
many cases, stem from the personalised approach that
allows for highly personalised learning for the
learner, but it is also necessary to have.
The Commission has identified
some shortcomings, including how to control misuse, however, few authors are
researching AI in these areas. Scientific production over the last five years
has been low and the number of scientific papers produced has been the main focus on the use and development of the same in certain
areas of degrees related to medicine, electronics or linguistics, but not in
how they apply this intelligence and its tools in new teaching methods.
It is also worth noting that
most of this scientific output is concentrated in Latin American countries and
that there has been virtually no research in Europe on AI and its use in
education, with the authors bearing little relation to each other in terms of
their work.
The concepts most studied by
scientists are grouped into three clusters where terms such as AI, outcomes, or
students are highlighted, but no study is done on the tools that allow AI to be
managed, which also means that these published studies usually remain mere
descriptions of the use of AI in certain learning situations.
On the contrary, social
listening gives primacy to the tools, leaving aside
concepts such as results or yields, which are not talked about in networks, nor
about its application in certain sectors, giving importance to how it should be
used in a tool such as GPT Chat. Feelings are neutral, which also indicates that
there is still a long way to go scientifically so that society can give its
opinion in networks and awaken emotions and feelings.
The study has several
limitations, the main one being that it was carried out on dates when
there was beginning to be talk of the use of some of
the AI tools in specific fields of the university, such as the development of
the so-called Final Degree Projects, or including in the conduct of tests and
examinations, which started to generate a debate on the need to change teaching
methodologies again. Possibly in the near future, lines of research stemming
from the IAC will focus on this, ignoring the misuse that can be made by
students and the excessive control to be performed by teachers to ensure that
this does not happen.
On the other hand, we have not carried out a
quantitative analysis, but a bibliometric analysis.
The WOS database and another
analysis based on social listening, but despite these limitations, this study
will allow a discussion on the EAI and, above all, how it is scientific studies
need to advance in terms of AI tools applicable to university studies so that
society can also have a say on it.
In short, the balance is going
to point to more benefits in terms of AI use than disadvantages, but scientific
studies are needed to demonstrate this for the whole of the university
community to start using AI tools regularly in their academic teaching processes,
as has been the case years ago with other types of progress technology, which
has subsequently become true allies of teachers, such as the m-learning, which
was able to take advantage of Internet content through devices mobile
electronic devices and has incorporated them as a new educational strategy.
Recent research suggests that
AI will be the breakthrough in education and training, the teaching-learning
process, as well as the driving force behind what is now being called the
"learning process".
Education 4.0 (Fidalgo-Blanco et al., 2022;
Ramírez-Montoya et al., 2022). Will this be the case and will
we be able to talk about Education 4.0 in a few years?
Author´s Contribution
Conceptualisation: S.M.-G. and A.B.-M.; Data curation:
S.M.-G.; Formal analysis: S.M.-G.; Research: A.B.-M.; Methodology: S.M.-G.;
Project management: A.B.-M.; Resources: A.B.-M.; Software: S.M.-G. and A.B.-M.;
supervision: S.M.-G. and A.B.-M.; validation: S.M.-G.; visualisation: A.B.-M.;
editing - original draft: S.M.-M.G.; proofreading and editing: S.M.-G. and
A.B.-M.
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