Impact and diversity in the use of generative articial intelligence tools
in journalism and communication: a comparative analysis
Impacto y diversidad en el uso de herramientas de inteligencia articial generativa
en el periodismo y la comunicación: un análisis comparativo
doxa.comunicación |
nº 41, pp. 535-559
| 535
July-December of 2025
ISSN: 1696-019X / e-ISSN: 2386-3978
How to cite this article:
Yaguana-Romero, H.; Galarza-Ligña, V. N. and Coronado-Otavalo, X. M. (2025). Impact and diversity in the use of generative articial intelligence
tools in journalism and communication: a comparative analysis.
Doxa Comunicación
, 41, pp. 535-559.
https://doi.org/10.31921/doxacom.n41a2875
Hernán Yaguana-Romero.
Ph.D. is Director of the Department of Communication Sciences at the Universidad Técnica
Particular de Loja (UTPL), Ecuador. He has a degree in Social Communication from UTPL, a Diploma in University
Productive Management, a Diploma of Advanced Studies and a PhD in Communication and Journalism from the
University of Santiago de Compostela (Spain), with SENESCYT registration No. 7474R-13-9696. Also, he is an expert
in distance education evaluation from the University of Seville. His career has been developed in teaching, consulting
and academic management. He has been a consultant for UNESCO and the National Council of Education, Science
and Technology of Ecuador (SENACYT). He has worked as a teacher-researcher at UTPL and other institutions such as
Universidad del Azuay and Universidad Casa Grande. He is a reviewer and international co-editor of indexed journals
such as Comunicar, and an active member of academic networks such as ALAIC, OCLACC and SEICOM. His lines of
research include digital storytelling, development communication and audiovisual media. He is the author of books
and manuals on radio, community communication and digital media, and has won awards for his radio production.
Professional achievements: Patented invention: Anti-stress headphones (International Patent Code WO/2020/039415).
Universidad Técnica Particular de Loja, Ecuador
[email protected]
ORCID:
0000-0001-6482-5333
Viviana Noemí Galarza-Ligña.
Master’s Degree in Communication and Creative Industries from the University of
Santiago de Compostela and Bachelor’s Degree in Social Communication from the Ponticia Universidad Católica del
Ecuador, Ibarra. Teacher in the area of Social Communication in the subjects of Sound Languages, Radio Production,
Educational, Community and Alternative Radio, Communication for Development, Socialmedia, Multimedia
Production, among others. Researcher in Communication, with experience in the area of Radio and New Technologies
and more than 30 publications in national and international journals, as a result of her participation in several research
projects on dierent aspects of the communicational activity. Member of the METACOM Research Group and of several
Scientic Committees of national and international congresses. Work experience in the eld of radio production,
institutional and community communication, as well as in the promotional management of private and community
institutions. Responsible for the Internationalization Oce at PUCE Ibarra.
Ponticia Universidad Católica del Ecuador Sede Ibarra, Ecuador
[email protected]
ORCID:
0000-0003-2770-246X
is content is published under Creative Commons Attribution Non-Commercial License. International License CC BY-NC 4.0
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
536 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
Recibido: 24/12/2024 - Aceptado: 04/06/2025 - En edición: 19/06/2025 - Publicado: 01/07/2025
Resumen:
Esta investigación analiza el impacto en cuanto a la percepción de efec
-
tividad profesional, la satisfacción y las diferencias en el uso de herra
-
mientas de inteligencia articial (IA) por periodistas y comunicadores
de Ecuador. Se utilizó un cuestionario estructurado que recopiló datos
de 150 periodistas y comunicadores de diversas especialidades. El cues
-
tionario incluyó secciones sobre el uso de IA, percepción de efectividad,
satisfacción e interés en capacitación. Los resultados revelaron una co
-
rrelación positiva entre la frecuencia de uso de herramientas de IA y la
satisfacción de los usuarios. Los encuestados que usan IA a diario repor
-
taron los niveles más altos de satisfacción, sugiriendo que la familiari
-
dad y la competencia adquiridas con el uso regular contribuyen signi
-
cativamente a la satisfacción. El estudio mostró que los periodistas y
creadores de contenido utilizan IA para la redacción de artículos, mien
-
tras que los especialistas en marketing tienen un uso más diversicado,
incluyendo investigación y análisis de datos. Los radialistas y fotógrafos
preeren herramientas de IA para la edición de contenido multimedia.
Estos hallazgos subrayan la importancia de desarrollar programas de
capacitación especícos, adaptados al campo periodístico y comunica
-
cional, así como la necesidad de lineamientos para un uso ético y perti
-
nente de herramientas de IA especializadas.
Palabras clave:
Inteligencia Articial; periodismo; comunicación; efectividad
profesional; desarrollo profesional.
Received: 24/12/2024 - Accepted: 04/06/2025 - Early access: 19/06/2025 - Published: 01/07/2025
Abstract.
is research analyses the impact on the perception professional
eectiveness, satisfaction and dierences in the use of articial
intelligence (AI) tools by journalists and communicators in Ecuador. A
structured questionnaire was used to collect data from 150 journalists
and communicators from various specialties. e instrument included
sections on AI use, perception of eectiveness, satisfaction and interest
in training. e results revealed a positive correlation between the
frequency of use of AI tools and user satisfaction. e respondents
who use AI daily reported the highest levels of acquiescence,
suggesting that familiarity and competence acquired through regular
use contribute signicantly to satisfaction. e study showed that
journalists and content creators use AI for article writing, while
marketers have a more diversied use, including research and data
analysis. Also, radio broadcasters and photographers prefer AI tools for
editing multimedia content. ese ndings underline the importance
of developing specic training programmes adapted to the journalistic
and communication eld, as well as the need for guidelines for an
ethical and relevant use of specialised AI tools.
Keywords:
Articial Intelligence; journalism; communication; professional
eectiveness; professional development.
Ximena Margarita Coronado-Otavalo.
Doctoral candidate in the interuniversity doctoral program in Communication
at the University of Seville. Master in Communication and Culture from the University of Seville. Master’s Degree in
New Technologies for Management and Teaching Practice from PUCE-Ibarra. Diploma in University Teaching and
Bachelor’s Degree in Social Communication. University teacher with 12 years of experience in journalism, radio,
print media, epistemology of research, research fundamentals, journalistic genres, digital communication and digital
culture. Researcher of topics related to cyberjournalism, social networks, analysis and structure of information, digital
culture, identity and migration. She has published articles in Latindex and Scopus regional indexing journals, as well
as book chapters. Member of the Network of Communication Researchers of Ecuador (RICE), of the researching group
Media, Applied Technologies and Communication (METACOM) and she has participated as organizer and member of
Scientic Committees of national and international congresses. Academic reviewer of scientic articles, books and book
chapters of various publishers and national and international scientic journals. Work experience in print media, radio,
organizational communication and communication for development.
Ponticia Universidad Católica del Ecuador Sede Ibarra, Ecuador
[email protected]
ORCID:
0000-0002-5146-0248
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 537
1. Introduction
Generative articial intelligence (GAI) has deeply transformed the eld of communication, modifying the processes of
production, distribution and reception of content. ese technologies automate tasks and redene professional routines,
information structures and relationships with audiences, emerging as a transforming agent of communication practice in all
areas, with technical opportunities and ethical challenges.
On the professional level, the media have started to integrate these tools at dierent stages of the workow. In Spain, a
recent study (Mayoral Sánchez, Parratt Fernández and Mera Fernández, 2023) revealed that 60% of the media already use AI,
especially in automated content distribution tasks. Furthermore, about 75% of the journalists surveyed expressed a positive
perception of these technologies. According to Mayoral Sánchez et al. (2023) “from rejection or fear, they are perceived more
as an opportunity than as a danger” (p.821).
From the academic eld, there is a growing interest on the part of universities and public bodies in researching the applications
and eects of AI in journalism. Murcia Verdú et al. (2024) highlight that “the documentary analysis of project reports evidences
that the eects of AI are being addressed not only from a technical point of view, but also from its socio-cultural and ethical
impact” (p.5).
However, this development requires for caution; Saad and dos Santos (2023), in an experiment with ChatGPT, identied risks
associated with automated content generation and stress that “the indispensable role of the human being in journalistic
processes was highlighted through the functions of editing and active control” (p.783).
1.1. eories on the adoption of technology in communication
e incorporation of emerging technologies, such as articial intelligence (AI), has prompted renewed interest in theoretical
models that explain how individuals and organisations adopt new tools. In the eld of journalism and communication, this
process involves cognitive, social, cultural and aective variables. Among the classic frameworks stands out, the Diusion of
Innovations eory (Rogers, 1983), which identies ve stages for adoption: knowledge, persuasion, decision, implementation
and conrmation. is model highlights the role of communication and the perceived characteristics of the innovation in the
propagation of technological change.
Complementarily, Structural Adaptation eory (DeSanctis and Poole, 1994) explains that technology, when is introduced
into organisations, it is transformed by means of interaction with users. is approach recognises that technological tools and
human dynamics shape each other, generating new organisational structures.
From the eighties, technology acceptance models oered a more accurate perspective on users’ disposition towards technology.
An example is the Technology Acceptance Model (TAM), proposed by Davis, Bagozzi and Warshaw (1989), which, as explained
by Hwang and Seo (2024) “suggests that perceived utility and ease of use signicantly inuence user acceptance of technology,
thereby reducing resistance” (p.422). is framework has demonstrated useful in communication contexts marked by constant
innovation.
538 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
Subsequently, more advanced models such as TAM3 and the Unied eory of Acceptance and Use of Technologies
(UTAUT-2) (Venkatesh et al., 2012) incorporated new variables such as hedonic motivation, habit and facilitating conditions.
ese approaches broaden the analysis by considering not only technical, but also emotional, social and organisational
aspects. Kacperski et al. (2025) argue that “user characteristics, including demographics and facilitating conditions [...] play a
substantial role in the adoption and use of digital technologies” (p.3), highlighting that age, educational level and institutional
environment are determinant factors for the acceptance of AI tools.
Due to, as technology develops, user experience becomes central, Gangadharaiah et al. (2023) propose the Pooled Rideshare
Acceptance Model (PRAM), based on TAM and UTAUT, adapted to digital environments where perceptions of privacy, security
and convenience are inuential. In their study they conclude that “perceived usefulness and perceived ease of use were
signicant constructs of user trust and acceptance” (p.11). ese variables are transferable to communication environments,
where the use of technologies such as AI is mediated by user trust and perceived control.
For Bhooma et al. (2025), the role of training is a fundamental variable as “training goes beyond the transmission of knowledge
to become a global intervention mechanism that inuences other antecedents such as attitude, expectations, facilitation and
motivation” (p.9). is implies that guided experience can positively modify attitudes towards technology, strengthening
sustained adoption of technology management.
An emerging theoretical contribution comes from Social Capital eory; Udo, Bagchi and Kirs (2023) who showed that moral
trust, civic engagement and life satisfaction inuence directly in the use of information technologies. In a multinational study,
they concluded that “higher levels of ICT use are associated with higher levels of trust (especially moralistic), greater civic
engagement and higher life satisfaction” (p.215), complementing traditional models by introducing relational and contextual
dimensions that also condition technological adoption, especially in communication environments.
is theoretical evolution allows to recognise that the acceptance of technologies such as AI depends not only on their
technical characteristics, but also on the social environment, institutional support, organisational culture and community ties
surrounding the user.
1.2. Impact of AI on perceptions of professional eectiveness
Technological adoption models facilitate the understanding of the way that the implementation of articial intelligence-
based tools impacts on the perception of professional eectiveness in journalism and communication, redening productive
dynamics to contribute to a more strategic valuation of work.
Automation, for example, has gained a prominent place in newsrooms, allowing media such as Globo in Brazil to automatically
generate thousands of stories based on ocial data, covering local events with an eciency without precedented (Shaw, 2021).
e ability of AI to handle large volumes of data and produce content quickly and accurately has transformed the perception
of professional eectiveness in journalism, increasing productivity and the quality of work done. is phenomenon, called
“automated journalism”, has led newsrooms to adopt a more technical approach to news production, where journalists become
supervisors of systems that generate content (Calvo-Rubio and Ufarte-Ruiz, 2020).
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 539
us, it is undeniable that AI facilitates the production and accuracy of content, but it also sets out ethical risks and
technological limitations. Ross et al. (2024) points out that “the challenge for risk communicators is how to redirect people to
credible sources” (p.12), which evidences the loss of control over information quality and credibility of information. Moreover,
automation has not yet reached the narrative depth that characterises human work; as Ufarte Ruiz and Manfredi Sánchez
(2019) point out, “it still does not generate complex texts, a human quality among the many that robots still lack” (p.231).
Despite the challenges, the journalism sector recognises the need to adapt to technological changes to remain relevant. As
Essenfelder et al. (2019) points out, “automation is presented as an alternative to release journalists from mechanical and
repetitive tasks, allowing them to focus on higher value-added activities such as analysis and interpretation data” (p.259).
is transition towards journalism focused on analytical functions, evidences a change in the perception of professional
eectiveness driven by the capabilities of articial intelligence in routine tasks.
1.3. e correlation between the use of AI and the perception of eectiveness: previous studies
Previous research has shown that articial intelligence (AI) signicantly inuences the way professionals perceive their
own performance. is impact can be both positive and negative, depending on its implementation and use. In the eld of
journalism, automation has made newsrooms operate more eciently, Rojas Torrijos and Toural Bran (2019) note that “the
use of
bots
in newsrooms automates repetitive tasks, allowing journalists to dedicate to functions that require more elaboration
and contribute value-added” (p.247).
e perception of increased productivity is not free of tensions. Ufarte Ruiz and Manfredi Sánchez (2019) warn that AI “has not
yet generated complex texts, a human quality among the many that robots still lack” (p.231).
On the other hand, the appreciation of eectiveness is also linked to job security. While automation generates concerns about
job substitution, it can also reinforce job satisfaction when it allows for a focus on more strategic tasks. As Gangadharaiah et al.
(2023) indicate, “AI-based personality development tools are often well received and considered eective by users, reinforcing
the idea that AI can provide signicant assistance in improving personal and social skills” (p.10), this idea evidences that
condence in the AI tool is key to eective integration and perception of positive performance
Other studies such as Kong et al. (2024) and Bhooma et al. (2025) show that sense of professional ecacy is related to resilience
to automation and adaptability. As Bhooma et al. (2025) state, “Acceptance of digital transformation of government (TGD)
requires major trust among employees, a positive perception of value and a sense of self-ecacy” (p.6).
1.4. Evolution and uses of AI in communication management
e advance of articial intelligence (AI) has marked a turning point in the dynamics of the contemporary media ecosystem.
Since its rst uses in newsrooms for automated news production to its integration into strategic communication and marketing
processes, AI has transformed both the operation and the professional logic of journalism and related sectors.
One of the milestones that marked the beginning of automated journalism was the launch of a sports news generation service
by e Big Ten Network in 2010, in collaboration with Fox Networks (Canavilhas and Giacomelli, 2023). Subsequently,
experiences such as that of the
Los Angeles Times
and the Associated Press consolidated the use of software to generate news
540 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
content without human intervention, allowing for an expansion of news coverage and an increase in productivity, especially
in areas such as economics or sports. From 2019, this trend intensied globally, incorporating new functionalities such as
data verication, trend analysis and content personalisation. Along these lines, Trang et al. (2024) state that “the adoption of
AI in these sectors has numerous advantages, including improving the eciency of journalists and the competitiveness of the
media.” (p.2), contributing to improve the production processes and the strategic position of the media.
e study carried out by Tejedor et al. (2025) corroborates that this adoption process has spread worldwide, with particular
intensity in media in the United States, Spain and the United Kingdom, “the technology imposed by AI is changing the media
towards new models focused less on reach and more on interaction, through more personalised content” (Tejedor, 2025, p.
21). In addition, it is documented that more than 100 media outlets use AI for tasks such as headline generation, user analysis
and video editing, which consolidates its strategic role in contemporary media production.
In this evolution, AI has also inuenced media organisational structures. Traditional newsrooms have had to adapt to new
workows, permanent sta training and the redenition of professional roles. is is conrmed by Al-Kfairy (2025) when he
indicates that “generative AI is revolutionising the functionating of organisations, oering transformative capabilities ranging
from automated content creation to strategic decision making” (p.1).
e impact of AI has transcended the information sphere and has extended to other areas such as marketing and institutional
communication. Tools based on generative models allow the automated creation of advertising texts, strategic reports and
personalised audiovisual content. is has radically transformed the production of messages in digital environments. However,
new digital skills are required, as well as professional supervision to ensure the coherence of the message.
In international media such as
e Washington Post or e New York Times
, these technologies have been adopted to free
journalists from repetitive tasks and expand coverage in real time (Aramburú Moncada et al., 2022). is has led professionals
to concentrate on analysis, interpretation and verication, which increases the added value of journalistic work. According to
Calvo-Rubio and Ufarte Ruiz (2020), this dynamic has transformed the productive routines, generating a broad reorganisation
of the informative task.
Nevertheless, the accelerated integration of AI has also revealed tensions inherent in a transition process. Simon (2024)
warns of a growing dependence of media on technology platforms; “AI recongures publishers’ dependence on platform
companies, exacerbating existing dependencies in distribution and creating new dependencies in production” (p.149). ese
dynamics, although not detracting from the benets of AI, invite reection on the need for regulatory frameworks and models
of technological governance adapted to the communications environment. Pinilla and Valle (2025) also highlight the need for
a discussion based on ethical principles of the way which AI impacts on the social rationale of the profession, recognising that
automation must be implemented without weakening the reective and critical role of journalism.
From another perspective, the use of AI has opened up debates about the emotional experience of the public. Technologies
such as Deep Nostalgia have given rise to automated content that appeals to emotion, but also generates perceptual
dissonance. Kidd and Nieto McAvoy (2023) warn that “the shift from photo to video in Deep Nostalgia modies and remediates
memory to make it both real and unreal, immediate and remote, comforting and disturbing at a time.” (p.622), revealing how
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 541
articial intelligence recongures the authenticity of media experience. erefore, the integration of AI into communication
management continues to advance, but requires that media autonomy and content integrity be prioritised.
1.5. AI tools for communication and journalism
e advance of articial intelligence (AI) has introduced tools that are redening journalistic and communicational work.
ese technologies, aimed at automating and optimising processes, have become structural elements that inuence every
phase of the news cycle. Tejedor and Vila (2021) arm that “the irruption of articial intelligence has transformed the way
of capturing, processing, generating and distributing information” (p.830), promoting a model of journalist empowered by
technology.
One of the most visible applications is robot journalism, which allows generating content from structured data using predened
algorithms. Although this improves accuracy and speed, it poses challenges in terms of creativity and narrative depth. As
Opdahl et al. (2023) explain, “Robotic journalism often employs rule-based approaches to NL generation from structured data
from highly trusted sources, such as public databases” (p.11), which evidences its technical value, although it diminishes the
expressive richness of journalistic language.
Likewise, AI makes it possible to personalise the news experience by adapting content to the user’s preferences. Simon (2024)
notes that “AI is used to personalise content, enhance the product experience and increase user engagement” (p.3), which has
redened digital media strategies.
1.6. Recurrent tools for tasks linked to the professional area
Articial intelligence (AI) tools used in journalism and communication can be grouped according to the professional tasks
or activities that facilitate the practice of this profession. is categorisation allows for a clearer understanding of how these
technologies are integrated into workows and how they are transforming professional practice in these elds.
–
Automatic content generation:
Chat GPT, Heliograf and Quakebot are examples of how AI is transforming journalism. e
Washington Post’s Heliograf generates articles automatically from structured data. On the other hand, the Los Angeles Times’
Quakebot reports on earthquakes using data from the United States Geological Survey (USGS). e tools improve eciency
in news writing, allowing journalists to focus on more reasoned tasks (Gómez-Calderón and Ceballos, 2024).
–
Transcription and audio processing:
In the eld of audio transcription and verication, Trint converts recordings into text
quickly, facilitating the work of journalists (Gómez-Calderón and Ceballos, 2024). In addition, VericAudio detects audio
deepfakes
to combat misinformation, ensuring the authenticity of sources.
–
Visual content generation:
MidJourney and Data Skrive are transforming visual content creation in journalism. MidJourney
generates digital images from text, providing attractive visual content without the need for design skills, and Data Skrive auto
-
mates the creation of graphics and visualisations based on data, optimising reports (Jaruga-Rozdolska, 2022).
542 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
–
Translation and reformulation of texts:
DeepL and AI Writer are outstanding tools. DeepL translates accurately and allows
the reformulation of texts to improve clarity and style (García Sánchez, 2023). AI Writer, on the other hand, focuses on the
generation of written content, producing drafts, summaries and advertising texts (Fernández, 2023).
Among the tools presented during the realisation of this research, some of the tools that have emerged as the most used by journa
-
lism and communication professionals are:
–
ChatGPT:
It is a language model based on transformer architecture, capable to generate text similar to a human from simple
prompts. Widely used in writing articles, emails and social media content, ChatGPT has proven to be a valuable tool for over
-
coming writer’s block and generating high-quality content in a short time (Fernandez, 2023).
–
MidJourney:
It allows the creation of digital images from textual descriptions. Developed on the Python programming lan
-
guage, MidJourney stands out for its ability to produce high-quality visual illustrations, making it an ideal choice for journa
-
lists and communicators who need to create attractive visual content quickly (Jaruga-Rozdolska, 2022).
–
Trint:
It converts audio recordings into text with high accuracy. e application saves time and eort, allowing professionals
to concentrate on content creation. Its ability to recognise multiple languages and accents makes it especially useful in a glo
-
balised environment (Gómez-Calderón and Ceballos, 2024).
–
Wibbitz:
It automatically generates video from text, allowing journalists to create visual content without the necessity of ad
-
vanced editing knowledge (Gómez-Calderón and Ceballos, 2024).
–
Bot Sentinel:
It classies and tracks inauthentic accounts and toxic trolls on social networks, using machine learning. It is
especially useful for communicators who manage the social media presence of brands or media outlets, helping to maintain
a cleaner and more trustworthy online environment (García Sánchez, 2023).
–
DeepL:
It is primarily known as a translation tool, DeepL oers highly accurate translations and also allows for the reformula
-
tion of texts to improve clarity and style. is is especially useful for journalists that work with sources in multiple languages,
facilitating access to global information (García Sánchez, 2023).
–
Jasper:
It allows journalists and communicators to create everything from articles to marketing campaigns, optimising texts
for SEO and customising them according to the specic needs of the target audience. It generates high-quality content in large
volumes (Fernandez, 2023).
1.7. Adapting AI to journalistic management
As part of the study, the main articles published from January to August 2024 by the international network of journalists the use and
projection of articial intelligence (AI) in the eld of journalism were collected and analysed. ese articles provide a real-world
perspective on how AI is being used in professional practice and how it is projected to evolve in the near future. Next, some relevant
examples are presented.
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 543
–
Operation Retuit
in Venezuela, led by the organisation Connectas, that uses AI-generated avatars to avoid the regime’s cen
-
sorship and protect the safety of journalists. is project uses characters such as La Chama and El Pana, who, although are not
real, present veried and high-quality content created by journalists (Cascante, 2024).
–
Research
. e use of AI for journalistic investigation in an automatic way has been signicant. An example of this is Quartz
which used optical character recognition (OCR) to read thousands of documents in its
Mauritius Leaks
investigation. Similar
-
ly, CIPER used Google’s Pinpoint to analyse information in multiple formats, facilitating complex investigations with limited
human and monetary resources, as mentioned by Ana Paula Valacco (cited in Cabral, 2024).
–
e “Digital butler” JAMES
, used by e Times and e Sunday Times to personalise newsletters, show the potential of AI
to improve connection with audiences. JAMES allows to adapt content to the specic interests of readers, reducing the rate of
newsletter unsubscribes and enhancing the relevance of the content oered (Chauvet, 2024).
e examples analysed reect how AI is being integrated into journalism in an eective and strategic way, allowing media to
improve their eciency, protect their professionals, and connect more deeply with their audiences.
According to Nic Newman’s report “Journalism, Media, and Technology Trends and Predictions 2024”, AI is destined to be
a disruptive force in content production and distribution, which will signicantly change the dynamics of traditional and
digital journalism. Also, articial intelligence is seen to have a considerable impact on media business models. Many news
organisations are exploring how best to monetise their content in an environment where AI can generate and distribute
information on a large scale. For example, licensing agreements are being established between media and AI platforms,
such as the one signed by Axel Springer with OpenAI, which includes payments for the use of historical and current content.
However, there is scepticism about whether these arrangements will benet all industry players equally. As the Reuters
Institute report warns, “Industry has the opportunity to work with AI actors to design a symbiotic ecosystem, and we must not
miss it” (Newman, 2024, p.16).
For this reason, it is essential to use AI as a language base and not as a primary source of knowledge. is caution can ensure
that AI complements, not undermines, the fundamental principles of journalism.
1.8. Ethical considerations
e incorporation of articial intelligence (AI) in the eld of communication sets out important ethical challenges. e
automation of content transforms the production, distribution and consumption of information, generating concerns about
transparency, algorithmic bias, professional responsibility, privacy, copyright and technological equity (Franganillo, 2023b).
One of the main challenges lies in information transparency. e ability of AI to generate text, images or videos like human
requires that public to clearly distinguish between human and automated content. According to Franganillo (2023b) opacity in
this aspect can erode trust and favour the circulation of misleading information, added to this Gutiérrez-Caneda et al. (2024)
indicates that “the integration of AI, especially when talking about generative AI, raises questions related to algorithmic and
data bias, transparency of the models used, data privacy, human supervision or even the possible loss of jobs.” (p.6), which
obliges communicators to act with ethical responsibility in the face of these tools.
544 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
Likewise, the debate about intellectual property arises in contexts where models are trained on large volumes of publicly
accessible data, but which may contain protected works. is practice sets out tensions regarding consent and fair compensation
(McClausland and Salgado, 2022). Ienca (2023) warns that these forms of technological intervention may even transgress
cognitive autonomy and “may violate some fundamental principles of freedom or right related to the dominance of a person’s
brain and mind” (p.839).
In parallel, there are concerns about the perpetuation of biases and stereotypes. When AI models learn from biased data, they
can reproduce discriminatory logics based on gender, ethnicity or culture (UNESCO & IRCAI, 2024). Moreover, the emotional
and anthropomorphic design of these systems generates new forms of interaction that can distort user perception. Stark (2024)
sums it up: “ChatGPT is a sophisticated textual animation [...] designed to give the system a greater appearance of vitality and
personality” (p. 6), questioning the ethical relationship between appearance of agency and generated content.
On the other hand, there is a risk that AI will accentuate structural inequalities in the media ecosystem. Organisations with
greater resources have easier access to these technologies, leaving smaller or peripheral entities behind. Opdahl et al. (2023)
note that “trustworthy AI-based journalism requires a delicate balance between AI-enhanced human tasks and AI-automated
routine tasks” (p.2).
us, it is essential that professionals adopt a critical and reective approach to the implementation of these technologies,
ensuring that they are used in a way that respects the fundamental values of society and promotes equity and justice in the
production and distribution of information content.
2. Methodology
e purpose of this research was to analyse how the use of articial intelligence (AI) tools inuences the perception of
professional eectiveness, user satisfaction and the dierences in their application according to the dierent functions
performed by journalists and communicators in Ecuador.
H1: Professionals who use AI tools designed for predictive analytics perceive a greater impact on their professional
eectiveness.
H2: ere is a positive correlation between the frequency of use of AI tools and the satisfaction reported by users in the
performance of their work tasks.
H3: ere are signicant dierences between the frequency of use of AI tools and the user’s profession or speciality.
e study design adopted a non-experimental quantitative approach, using a structured survey as the main data collection
instrument. e questionnaire was designed to capture detailed information on the main variables, including type of tools
used, frequency of use, perception of professional eectiveness, level of satisfaction and interest in receiving additional
training. Before the nal application of the questionnaire, a validation was carried out through a pre-test in order to ensure
the clarity, relevance and contextual appropriateness of the questions asked. is pre-test was applied to a group of 23 people:
professional journalists who are members of the National Union of Journalists (
UNP- Unión Nacional de Periodistas
) in Loja
city, as well as students and graduates of the communications career of the Universidad Técnica Particular de Loja (UTPL).
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 545
e choice of this group was based on its geographical proximity and the possibility of having professionals familiar with
the dynamics of journalism and communication. e validation revealed certain aspects of the questionnaire that needed
adjustments. In particular, it was noted that some questions were not completely clear in their wording or did not precisely
match the professional context of the participants. Suggestions collected during the pre-test allowed us to reformulate those
questions that were ambiguous, as well as to reduce the number of questions, in order to optimise the respondent’s experience
and ensure the quality of the data collected.
e population, subject of the study, included Ecuadorian journalists and communicators who incorporate articial intelligence
(AI) tools in their professional activities. Given the exploratory nature of the research and the diculty of accessing an updated
national registry of professionals in the sector who use AI, a non-probabilistic convenience sampling approach was chosen.
is approach allowed us to eectively reach a diverse group of participants within a limited collection timeframe and with
moderate logistical resources.
e nal sample was made up of 150 professionals, and an attempt was made to ensure diversity both in terms of profession
(journalists, communicators, content creators, marketing specialists, radio broadcasters, photographers) and in terms
of specialisations and work contexts (traditional, digital, institutional and freelance media). To broaden geographical
representativeness, participants from several provinces of the country were included, mainly from Loja, Pichincha, Guayas
and Azuay. is was achieved by distributing the survey through professional networks, trade associations (such as the
National Union of Journalists-
Unión Nacional de Periodistas
), groups specialised in communication on social networks, and
dissemination lists linked to universities and media outlets.
Although the sample does not pretend to be statistically representative of the entire population of communicators in Ecuador,
it does seek to reect the heterogeneity of the current communication ecosystem, with emphasis on those professionals
who have begun to integrate AI tools into their work routines. is approach is consistent with previous studies that explore
emerging technological phenomena in professional elds, where usage practices are still in consolidation and access to the
total population is limited.
In order to ensure the internal consistency of the applied questionnaire, the Cronbach alpha reliability index was calculated
for the two key sections of the instrument: perception of professional eectiveness and satisfaction with the use of articial
intelligence (AI) tools. ese dimensions were selected because of their direct relevance to the hypotheses raised and for
representing latent constructs that required reliable measurement.
e analysis was performed in the statistical software R Studio, using the Cronbach. Alpha() function of the psych package.
e results obtained showed coecients of
α
= 0.81 for the perceived eectiveness scale and
α
= 0.77 for the satisfaction scale,
indicating good internal consistency in both cases. According to the specialised literature, values above 0.70 are considered
acceptable for exploratory purposes in the social sciences (Nunnally and Bernstein, 1994), which supports the reliability of the
instrument used in this research.
ese results reinforce the methodological validity of the study by ensuring that the measurements consistently reect
participants’ perceptions of the professional impact of AI in their work environments. is statistical validation, combined
with the previously described qualitative pre-test, guarantees the robustness of the questionnaire as a data collection tool.
546 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
2.1. Data collection instrument
To obtain information that responds to the objectives and hypotheses raised, a structured questionnaire was used that included
the following sections:
–
Demographic data: Age, profession.
–
Use of AI: Type of AI tools used, frequency of use.
–
Perception of professional eectiveness: How respondents perceive the eectiveness of AI tools in their work.
–
Satisfaction: Level of satisfaction with the use of AI tools.
–
Interest in training: Interest in receiving additional AI training.
e code card was established according to the hypotheses that seek to obtain quantitative-qualitative data on the resources
used by Ecuadorians.
For the construction and application of this instrument, the process described below was followed:
–
Questionnaire design:
e questionnaire was designed to include closed and multiple-choice questions, facilitating the
quantication of responses.
–
Instrument validation:
e questionnaire was validated through a pre-test with a small group of journalists, communicators
and students in their nal years of communication studies to ensure its clarity and relevance.
–
Survey distribution:
e survey was distributed electronically via email and social media platforms specically for journalists
and communicators.
–
Data collection:
Data were collected over a period of 8 weeks (February and March 2024).
–
Data analysis:
Data collected were analysed using descriptive and inferential statistical techniques. Statistical software (R
studio) was used to perform correlation analysis and comparison of means.
–
e following data analysis techniques were used:
–
Descriptive analysis:
It was used to describe the demographic characteristics of the respondents and the frequencies of AI
tool use.
–
Correlation analysis:
It was used to evaluate the relationship between the type of AI tool most used and the perception
of professional eectiveness (Hypothesis 1), as well as the relationship between frequency of AI tool use and satisfaction
(Hypothesis 2).
–
Analysis of variance (ANOVA):
It was applied to identify signicant dierences in the type and frequency of AI tool use
between the dierent professions (Hypothesis 3).
In addition, in order for the research to meet the required ethical standards, the following aspects were taken into account:
–
Informed consent:
Informed consent was obtained from all participants prior to data collection.
–
Condentiality:
e condentiality of participants’ responses was ensured, keeping the data anonymous and secure.
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 547
–
Voluntariness:
Participation in the survey was completely voluntary, and participants could withdraw at any time without
repercussions.
3. Results
e presentation of results and the interpretation of the data is organised in three sections that respond to each of the
hypotheses designed:
3.1. Relationship between type of AI tool used and perceived professional eectiveness
For this analysis, percentage correlations were calculated between the dierent types of AI tools used and the perception of
professional eectiveness reported by respondents.
Figure 1. Correlation between AI and perceived eectiveness
Source: surveys applied
548 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
e results of correlations in gure 1 reveal that the MidJourney AI tool is perceived as highly eective by 75% of respondents
who use it, while Copilot and Bard are perceived as highly eective by 60% and 65% of users, respectively. ChatGPT also
has a high perception of eectiveness, with 70% of users considering it highly eective, although a small percentage (5%)
consider it not eective. On the other hand, Jasper and Writesonic show smaller percentages of high eectiveness (55% and
50%, respectively) and relatively high percentages of medium eectiveness (35% and 40%, respectively).
ere are several endogenous factors that could explain these results. First of all, the specic functionalities and adaptability
of the tools play a crucial role. MidJourney, for example, seems to oer specic tools for creative tasks that are highly valued
by journalists and communicators. Similarly, ChatGPT is appreciated for its versatility and ability to generate coherent and
relevant content in multiple contexts. Ease of use is also an important factor; tools such as MidJourney and ChatGPT can have
intuitive user interfaces and simple integration processes, facilitating adoption and regular use. Furthermore, the frequency
and quality of software updates and the technical support oered can inuence perception of eectiveness, given thar tools
with strong support and constant improvements are likely to receive higher ratings.
Likewise, there are also exogenous factors that may be inuencing these perceptions. e reputation of the tool and the marketing
strategies employed can signicantly aect the perception of eectiveness. Tools such as MidJourney and ChatGPT can benet
from eective advertising campaigns and good reviews on public platforms.
e existence of an active user community and
the availability of resources such as tutorials, forums and guides can also improve the perception of eectiveness. Tools with a
strong user community, such as ChatGPT, provide a collaborative learning environment that improves the user experience. In
addition, compatibility with other systems and tools used by journalists and communicators can be a key factor. Platforms that
integrate easily with existing software in the professional workow are perceived as more eective.
Tools perceived as highly eective, such as MidJourney and ChatGPT, may see an increased adoption, which could lead to further
development and improvement of their functionality. Dierences in the perception of eectiveness can drive competition
among AI tool developers, encouraging innovation and continuous product enhancement. In addition, organisations can
focus on oering specic training in the use of highly rated tools to maximise their eectiveness in professional work, which
could include workshops, online courses and additional resources for users of MidJourney, ChatGPT, and other prominent
resources. Finally, tools that do not achieve high eectiveness ratings may need to be evaluated and their functionality updated
to favourably accomplish the needs of users, based on feedback
received.
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 549
3.2. Correlation between the frequency of use of AI tools and the satisfaction of journalists and communicators
e following graph shows the percentage correlations between the frequency of use of AI tools and the satisfaction reported
by respondents:
Figure 2. Correlation between frequency and satisfaction
Source: surveys applied
550 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
Pearson’s correlation analysis between frequency of use of articial intelligence (AI) tools and professional satisfaction
showed a correlation coecient of -0.11 (p=0.09), indicating that there is no statistically signicant relationship between the
two variables. However, a descriptive analysis of the trends in the data suggests that satisfaction could vary with frequency of
use, revealing patterns that may be notable for future research.
As can be seen in Figure 2, users who employ AI tools on a daily basis report the highest levels of satisfaction: 70% described as
very satised and 25% as satised, while only 5% expressed little satisfaction and no users were dissatised. is group stands
out for their high familiarity and competence, which seems to translate into a more positive perception of the tools used.
On the other hand, those who use AI tools weekly maintain high overall satisfaction, with 50% of users very satised and 40%
satised. Nevertheless, the remaining 10% were considered little satised, reecting a decrease compared to daily users. is
trend could be attributed to less consistent use, which could limit familiarity with the advanced features of these technologies.
Users who use AI tools less frequently, such as those who use them monthly, show a greater dispersion in satisfaction levels.
While 30% are very satised and 50% satised, 20% express little satisfaction. is reduction in higher levels of satisfaction may
reect the diculty in maintaining a constant level of familiarity or practical skill due to sporadic use.
Finally, occasional users report the lowest overall satisfaction, with only 20% describing themselves as very satised and
40% satised. A signicant percentage (30%) indicated little satisfaction, and 10% declared themselves dissatised. is low
frequency of use seems to be associated with a lack of condence in handling the tools, which contributes to less positive
experiences.
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 551
3.3. Dierences in the type and frequency of use of AI tools between dierent professions
Figure 3. Dierences in the use of AI tools by profession
Fuente: Source: surveys applied
Note: e graph shows the percentage dierences in the type and frequency of use of AI tools among dierent professions
e analysis of variance (ANOVA) conducted to evaluate dierences in the frequency of AI tool use between the dierent
professions did not nd statistically signicant results (F=1.77, p=0.14). is indicates that, in general, the frequency of use does
not vary substantially between the professional groups analysed, since the p-value is greater than 0.05. However, a descriptive
analysis suggests some interesting trends in the usage patterns of AI tools, depending on the specic needs of each profession.
Figure 3 shows that journalists stand out for their high use of AI tools for article writing, with 80% of respondents indicating
this application as predominant. However, their use in other areas is limited: only 10% use AI for research, 5% for data analysis,
3% for video editing and 2% for social media. is reects the textual nature of their work and the reliance on AI tools focused
on written content generation.
In contrast, marketing specialists show a more diversied use of AI tools. 40% use them for article writing, 20% for research,
30% for data analysis and 5% for both video editing and social media. is pattern underlines the multidimensional nature of
marketing, which combines content creation, market research and strategic analysis.
552 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
Content creators also present high use of AI tools for article writing (70%), though with additional applications in research
(15%) and data analytics, video editing and social media (5% each). is suggests that, while they share similarities with
journalists, their work includes a wider range of tasks that leverage diverse AI capabilities.
On the other hand, radio broadcasters distribute their use of AI among various activities: 20% use it for writing news stories,
30% for research, 10% for data analysis, 30% for video editing and 10% for social media. is balanced distribution of usage
reects the multifaceted nature of their work, which combines research and multimedia production.
Finally, photographers focus on editing visual content, with 60% using AI tools for video editing. Other uses are less frequent:
10% for article writing and data analysis, 5% for research and 15% for social media. is pattern reects the specic demands
of photography, where image and video editing play a key role.
4. Discussion
e ndings of this study reveal patterns in the relationship between the use of articial intelligence (AI) tools and the
perception of eectiveness and professional satisfaction in the elds of journalism and communication in Ecuador. some
correlations were not statistically signicant, the descriptive data oer interpretative clues that merit analysis in the light of
theoretical frameworks such as the Technology Acceptance Model (TAM) and its extensions (TAM3 and UTUAT 2) and the
subsequent Pooled Rideshare Acceptance Model (PRAM), based on TAM and UTUAT2.
4.1. e relationship between frequency of use and professional satisfaction
While the correlation coecient between frequency of AI tool use and professional satisfaction was low (r = -0.11, p = 0.09),
the descriptive data indicate that those who use these technologies daily report consistently high levels of satisfaction. is
nding can be interpreted through the TAM3’s notion of “t” (Venkatesh et al., 2012), where perceptions of usefulness and
ease of use transform as experience with the technology increases. It is probably that frequent users have overcome the initial
learning curve and developed a degree of competence that allows them to integrate AI in a uent way into their work, raising
their perception of satisfaction.
Likewise, as Gangadharaiah et al. (2023) indicate, it is key to understand that over the years, a key element for the user is the
security they feel when using one AI versus another. Similarly, this result aligns with studies such as Kong et al. (2024), which
point out that positive perceptions of AI favour professional resilience and informal learning. In this sense, not only access to
tools, but prolonged and meaningful interaction with them, directly inuences the professional experience, as Hwang and Seo
(2024) clearly explain. Also, the concept of “meaningful adoption” of technologies comes into play, in which it is not enough
to use a tool, but it must be perceived as useful and coherent with the user’s work and cognitive goals (Davis, 1989; Orlikowski,
2000).
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 553
4.2. Dierences according to professions: beyond ANOVA
Although the ANOVA analysis did not show statistically signicant dierences between professions (F=1.77, p=0.14), the
patterns observed in the descriptive data indicate a functional segmentation of AI use. Journalists and content creators focus
mainly on writing texts; marketing specialists diversify their use towards research and data analysis; while radio broadcasters
and photographers prioritise, tools linked to multimedia editing. ese results coincide with previous research (Túñez et al.,
2020; Calvo-Rubio and Ufarte-Ruiz, 2020) that warn about the transformation of professional routines based on the specic
capabilities of AI. is diversity in uses coincides with recent studies that point out that AI is adopted in a functional way
according to the operational logic of each professional eld (Napoli, 2022; Carlson, 2023).
is nding supports the proposal that the design of AI tools and training programmes should be adapted to the characteristics
of the specic professional eld. Instead of generalist solutions, contextualised specialisation is required, which maximises the
potential of AI according to the tasks and needs of each job prole linked to communication and journalism.
4.3. Implications and contributions to the academic debate
An important contribution of this study is its proposal to look beyond classical statistical indicators to recover the interpretative
value of emerging patterns of use. In a context where AI is in a state of early adoption or transition, understanding how these
early experiences of use are congured is essential to anticipate resistance, gaps or strategic uses. us, this work is inserted
in the academic debate on the dierential adoption of technologies, dialoguing with approaches such as the Structural
Adaptation eory (DeSanctis and Poole, 1994), which highlights the dynamic interaction between technological capabilities
and organisational structures.
Furthermore, Trang et al. (2024) highlight that the eectiveness of the media has increased in relation to the use of AI.
However, its use is reinforced by the need to consider qualitative and organizational factors that can modulate the perception
of eectiveness and satisfaction, such as professional culture, the type of media or the resources available for training and
updating.
However, the study also allows us to advice certain barriers that could be holding back a wider use of AI among communication
professionals. ese include a lack of specic training, limited customization of available tools, and, in some cases, resistance
to technological change. ese limitations coincide with the literature that warns that the implementation of AI in creative or
narrative elds used to face tensions related to professional autonomy and the perception of threat to human competencies
(Zamith, 2021; Beckett, 2023).
4.4. Future directions
e ndings of this research open up future lines of study around the adoption and perception of generative articial
intelligence (GAI) tools in communication contexts. Firstly, it would be pertinent to develop longitudinal studies that analyse
how the perception of eectiveness and professional satisfaction evolve as users gain experience and become familiar with
the tools. is approach would make it possible to empirically validate the postulates of the TAM3 model and even the PRAM
model in real contexts of communication management.
554 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
It is also necessary to explore how cultural, organisational and generational factors inuence the adoption and use of AI,
comparing national and international scenarios to identify global patterns and also specic dynamics to each region or type of
media, contributing to a more holistic understanding of the phenomenon.
Also, it is relevant to analyse the ethical, labour and deontological implications associated with the intensive use of AI in
journalism and communication. Aspects such as transparency in authorship, automated verication of information, copyright
protection, or the creation of misleading content generated by algorithms require priority attention in the academic and
professional debate.
It is important to investigate the impact of these technologies on productivity, but also on the quality, creativity and authenticity
of the content generated. Qualitative and experimental studies could shed light on whether AI tools are strengthening or
weakening the critical and humanistic practice of journalism, and how a balance can be achieved between technological
eciency and editorial principles.
5. Conclusions
is study oers an empirical look at the impact and diversity in the use of generative articial intelligence (AI) tools
in journalism and communication in Ecuador. rough a quantitative approach, it was analysed how the perception of
professional eectiveness, satisfaction and frequency of use varies in dierent areas of communication.
One of the ndings shows that professionals who use AI tools on a daily basis report higher level of satisfaction, which is
related to concepts from the Technology Acceptance Model (TAM3) and Pooled Rideshare Acceptance Model (PRAM), such
as accumulated experience and adjustment of perceptions through continuous use. correspondence suggests that familiarity
and progressive domain of the tools contribute to their functional integration into everyday work, as well as to a more positive
attitude towards their professional usefulness.
In addition, although no signicant dierences between professions were identied, usage patterns showed a clear
segmentation: journalists tend to focus on text writing, while marketing specialists, radio broadcasters and photographers
diversify their applications towards data analysis, research or multimedia editing.
e research underlines the need to design dierentiated training strategies according to the professional prole, promoting
an eective and contextualised use of AI technologies. It also highlights the importance of promoting digital literacy policies
that accompany technological evolution with a critical, ethical and humanistic approach. At a time when technologies are
advancing faster than the capacity for institutional adaptation, studies such as this one allow us to detect opportunities, gaps
and tensions.
Finally, this study has certain limitations that must be recognised. Firstly, the sample was obtained by convenience and
is concentrated in a specic geographical context (Ecuador), which limits the generalisability of the results. Secondly, the
absence of statistical signicance forces us to consider that the observed patterns could be inuenced by variables external to
this study. Furthermore, a single cross-sectional measurement was used, so causal inferences cannot be established and not
even to observe evolution over time. Despite these limitations, the study oers inputs for understanding how AI technologies
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 555
are being appropriated in communication professions and opens up lines of research that could be deepened in the future and
provide greater interpretative density.
6. Acknowledgements
e researchers are grateful for the institutional support of the Universidad Técnica Particular de Loja and the Ponticia
Universidad Católica del Ecuador Sede Ibarra. is article has been translated into English by Dr. Paola Seraquive, whom we
thank for her work.
In the same way, the nancial support for this Project by the Universidad Técnica Particular de Loja is acknowledged.
7. Specic contributions of each author
Name and Surnam
e
Conception and design of the workHernán Yaguana-Romero
Methodology
Hernán Yaguana-Romero
Data collection and analysis
Hernán Yaguana-Romero, Ximena Margarita Coronado-Otavalo and
Viviana Noemí Galarza-Ligña
Discussion and conclusionsHernán Yaguana-Romero, Ximena Margarita Coronado-Otavalo
Drafting, formatting, version review and approval
Hernán Yaguana-Romero, Ximena Margarita Coronado-Otavalo and
Viviana Noemí Galarza-Ligña.
8. Conict of interest
e authors declare that there is no conict of interest contained in this article.
9. Bibliographic references
Al-Kfairy, M. (2025). Strategic integration of generative AI in organizational settings: Applications, challenges and adoption
requirements. IEEE Engineering Management Review.
https://doi.org/10.1109/EMR.2025.3534034
Aramburú Moncada, L. G., López Redondo, I., y López Hidalgo, A. (2023). Inteligencia articial en RTVE al servicio de la
España vacía. Proyecto de cobertura informativa con redacción automatizada para las elecciones municipales de 2023. Revista
Latina De Comunicación Social, 81, 1–16.
https://doi.org/10.4185/RLCS-2023-1550
Beckett, C. (2023). Articial intelligence and journalism: Understanding the potential and risks. Reuters Institute for the Study
of Journalism.
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
556 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
Bhooma, V. G., Kumar, R. R., Paramasivan, K., & Kamalanabhan, T. J. (2025). Transforming traditions: How training shapes
employee acceptance of digital transformation in a large legacy government workforce. Cogent Social Sciences, 11(1), 2461744.
https://doi.org/10.1080/23311886.2025.2461744
Cabral, E. (2024, April 9). ¿Qué hacer (y qué no) para implementar la inteligencia articial en el periodismo? IJNet: Red
Internacional de Periodistas.
https://bit.ly/cabral2024
Calvo-Rubio, L.-M., y Ufarte-Ruiz, M.-J. (2020). Percepción de docentes universitarios, estudiantes, responsables de
innovación y periodistas sobre el uso de inteligencia articial en periodismo. El Profesional de La Información, 29(1), 1–14.
https://doi.org/10.3145/epi.2020.ene.09
Canavilhas, J., y Giacomelli, F. (2023). Inteligencia articial en el periodismo deportivo: Estudio en Brasil y Portugal. Revista de
Comunicación, 22(1), 53–69.
https://doi.org/10.26441/rc22.1-2023-3005
Carlson, M. (2023). Automating judgment? Algorithmic journalism and the redenition of journalistic authority. Digital
Journalism, 11(2), 145–163.
https://doi.org/10.1177/1461444817706684
Cascante, L. F. (2024, agosto 26). Operación Retuit: Presentadores creados con inteligencia articial para combatir la censura
en Venezuela. iJNet: Red Internacional de Periodistas.
https://bit.ly/cascante2024
Chauvet, R. (2024, March 28). La IA despierta esperanzas y temores en los medios locales. IJNet: Red Internacional de
Periodistas.
https://bit.ly/chauvet2024
Davis, F. D., Bagozzi, R. P., y Warshaw, P. R. (1989). User acceptance of user technology: A comparison of two theoretical
models. Management Science, 35(8), 982-1002.
https://doi.org/10.1287/mnsc.35.8.982
DeSanctis, G., y Poole, M. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory.
Organization Science, 5(2), 121-147.
https://doi.org/10.1287/orsc.5.2.121
Essenfelder, R., Canavilhas, J., Maia, H. C., y Pinto, R. J. (2019). Automatización de textos periodísticos en la televisión brasileña:
Estudio de caso del sistema AIDA (Globo-Brasil). Doxa Comunicación, 29, 255-274.
https://doi.org/10.31921/doxacom.n29a13
Fernández Rincón, A. R. (2023). El creativo invisible: Inteligencia articial y creación publicitaria. Miguel Hernández
Communication Journal, 14(2), 391-408.
https://doi.org/10.21134/mhjournal.v14i.1983
Franganillo, J. (2023b). El uso de modelos generativos en el periodismo escrito y audiovisual: Un nuevo paradigma en la
creación de contenidos. En F. J. Martínez Cano y A. M. Sáez de Urabain (Eds.), Galaxia Zuckerberg: La comunicación en la era
digital (pp. 249-269). Tirant lo Blanch.
https://bit.ly/fraganillo2023
Gangadharaiah, R., Brooks, J. O., Rosopa, P. J., Su, H., Boor, L., Edgar, A., Kolodge, K., & Jia, Y. (2023). e development of the
pooled rideshare acceptance model (PRAM). Safety, 9(3), 61.
https://doi.org/10.3390/safety9030061
García Sánchez, O. V. (2023). Uso y percepción de ChatGPT en la educación superior. Revista De Investigación En Tecnologías
De La Información, 11(23), 98–107.
https://doi.org/10.36825/RITI.11.23.009
Gómez-Calderón, B., y Ceballos, Y. (2024). Periodismo e inteligencia articial. El tratamiento de los chatbots en la prensa
española. Index.Comunicación, 14(1), 281-300.
https://doi.org/10.62008/ixc/14/01Period
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 557
Gutiérrez-Caneda, B., Lindén, C.-G., & Vázquez-Herrero, J. (2024). Ethics and journalistic challenges in the age of articial
intelligence: Talking with professionals and experts. Frontiers in Communication, 9, Article 1465178.
https://doi.org/10.3389/
fcomm.2024.1465178
Hwang, I., & Seo, R. (2024). Organizational support for digital transformation in the metaverse: A contingent pathway from
user experience to digitalization resistance. R&D Management, 55(2), 420–434.
https://doi.org/10.1111/radm.12706
Ienca, M. (2023). On Articial Intelligence and Manipulation. Topoi, 42(3), 833–842.
https://doi.org/10.1007/s11245-023-09940-3
Jaruga-Rozdolska, A. (2022). Articial intelligence as part of future practices in the architect’s work: MidJourney generative
tool as part of a process of creating an architectural form. Architectus, 3(71), 95-104.
https://doi.org/10.37190/arc220310
Kacperski, C., Ulloa, R., Bonnay, D., Kulshrestha, J., Selb, P., & Spitz, A. (2025). Characteristics of ChatGPT users from Germany:
Implications for the digital divide from web tracking data. PLOS ONE, 20(1), e0309047.
https://doi.org/10.1371/journal.
pone.0309047
Kidd, J., & Nieto McAvoy, E. (2023). Deep nostalgia: Remediated memory, algorithmic nostalgia, and technological ambivalence.
Convergence, 29(3), 620–640.
https://doi.org/10.1177/13548565221149839
Kong, H., Jiang, X., Zhou, X., Baum, T., Li, J., y Yu, J. (2024). Inuence of articial intelligence (AI) perception on career resilience
and informal learning. Tourism Review, 79(1), 219-233.
https://doi.org/10.1108/TR-10-2022-0521
Mayoral-Sánchez, J., Parratt-Fernández, S., & Mera-Fernández, M. (2023). Uso periodístico de la IA en medios de comunicación
españoles: mapa actual y perspectivas para un futuro inmediato. Estudios sobre el Mensaje Periodístico, 29(4), 821–832.
https://doi.org/10.5209/esmp.89193
McCausland, E., y Salgado, D. (2022, August 10). ¿Sueñan los androides con ovejas eléctricas? Este es el arte que ya está creando
la IA. El Grito.
https://bit.ly/mcausland2022
Murcia Verdú, F. J., Ramos Antón, R., y Calvo Rubio, L. M. (2022). Análisis comparado de la calidad de crónicas deportivas
elaboradas por inteligencia articial y periodistas. Revista Latina de Comunicación Social, 80, 91-111.
https://doi.org/10.4185/
rlcs-2022-1553
Napoli, P. (2019). Social Media and the Public Interest: Media Regulation in the Disinformation Age. New York Chichester,
West Sussex: Columbia University Press.
https://doi.org/10.7312/napo18454
Newman, N. (2024). Journalism, media, and technology trends and predictions 2024. Reuters Institute for the Study of
Journalism.
https://doi.org/10.60625/risj-0s9w-z770
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Opdahl, A. L., Tessem, B., Dang-Nguyen, D.-T., Motta, E., Setty, V., rondsen, E., Tverberg, A., y Trattner, C. (2023). Trustworthy
journalism through AI. Data y Knowledge Engineering, 146, 102182.
https://doi.org/10.1016/j.datak.2023.102182
Orlikowski, W. J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations.
Organization Science, 11(4), 404–428.
https://doi.org/10.1287/orsc.11.4.404.14600
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
558 |
nº 41, pp. 535-559
|
July-December of 2025
Impact and diversity in the use of generative articial intelligence tools in journalism and communication: a comparative...
ISSN: 1696-019X / e-ISSN: 2386-3978
doxa.comunicación
Pinilla Escobar, F. A., & Valle Jiménez, D. (2025). Inteligencia articial y periodismo en tiempos de incertidumbre y volatilidad.
Disertaciones: Anuario Electrónico de Estudios en Comunicación Social, 18(1), 1–16.
https://doi.org/10.12804/revistas.
urosario.edu.co/disertaciones/a.14288
Rogers, E. M. (1983). Diusion of innovations (3rd ed.). Free Press.
Rojas Torrijos, J. L., & Toural Bran, C. (2019). Periodismo deportivo automatizado. Estudio de caso de AnaFut, el bot desarrollado
por El Condencial para la escritura de crónicas de fútbol. Doxa Comunicación. Revista Interdisciplinar de Estudios de
Comunicación y Ciencias Sociales, (29), 235–254.
https://doi.org/10.31921/doxacom.n29a12
Ross, A. D., Siebeneck, L., Wu, H.-C., Kopczynski, S., Nepal, S., & Sauceda, M. (2024). Seven challenges for risk communication in
today’s digital era: e emergency manager’s perspective. Sustainability, 16(24), 11306.
https://doi.org/10.3390/su162411306
Saad, E., & Carneiro dos Santos, M. (2023). Jornalismo, inteligência articial e desinformação: avaliação preliminar do
potencial de utilização de ferramentas de geração de linguagem natural, a partir do modelo GPT, para difusão de notícias
falsas. Estudios sobre el Mensaje Periodístico, 29(4), 783–794.
https://doi.org/10.5209/esmp.87965
Shaw, R. (2021). Uso de inteligencia articial en los medios de comunicación de América Latina. El Clip.
https://bit.ly/
shawr2021
Shi, Y., & Sun, L. (2024). How Generative AI Is Transforming Journalism: Development, Application and Ethics. Journalism and
Media, 5(2), 582-594.
https://doi.org/10.3390/journalmedia5020039
Simon, F. M. (2024). Articial intelligence in the news: how AI retools, rationalizes, and reshapes journalism and the public
arena. Tow Center for Digital Journalism at Columbia University.
https://bit.ly/simonf2024
Stark, L. (2024). Animation and articial intelligence. Proceedings of the 2024 ACM Conference on Fairness, Accountability,
and Transparency (FAccT ’24), June 3–6, 2024, Rio de Janeiro, Brazil. ACM.
https://doi.org/10.1145/3630106.3658995
Tejedor Calvo, S., Cervi, L., & Vick Saurí, S. (2025). Cómo están adoptando la IA las empresas de comunicación. Comunicación
Y Sociedad, 1–27.
https://doi.org/10.32870/cys.v2025.8846
Tejedor, S., y Vila, P. (2021). Exo journalism: A conceptual approach to a hybrid formula between journalism and articial
intelligence. Journalism and Media, 2(4), 830-840.
https://doi.org/10.3390/journalmedia2040048
Trang, T. T. N., ang, P. C., Hai, L. D., Phuong, V. T., & Quy, T. Q. (2024). Understanding the adoption of articial intelligence in
journalism: An empirical study in Vietnam. SAGE Open, 14(2), 1–16.
https://doi.org/10.1177/21582440241255241
Túñez, M., Martínez-Solana, Y., & Abejón-Mendoza, P. (2020). Herramientas de inteligencia articial en el trabajo periodístico:
percepciones y actitudes de los profesionales. El Profesional de la Información, 29(4), e290423.
https://doi.org/10.3145/
epi.2020.jul.23
Udo, G. J., Bagchi, K. K., & Kirs, P. (2023). Does social capital play a role in information and communication technology
adoption? Issues in Information Systems, 24(4), 208–219.
https://doi.org/10.48009/4_iis_2024_116
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
doxa.comunicación |
nº 41, pp. 535-559
July-December of 2025
Hernán Yaguana-Romero, Viviana Noemí Galarza-Ligña and Ximena Margarita Coronado-Otavalo
ISSN: 1696-019X / e-ISSN: 2386-3978
| 559
Ufarte Ruiz, M. J., & Manfredi Sánchez, J. L. (2019). Algoritmos y bots aplicados al periodismo. El caso de Narrativa Inteligencia
Articial: estructura, producción y calidad informativa. Doxa Comunicación, 29, 213–233.
https://doi.org/10.31921/doxacom.
n29a11
UNESCO, & IRCAI. (2024). Challenging systematic prejudices: An investigation into gender bias in large language models.
UNESCO; International Research Centre on Articial Intelligence (IRCAI).
https://bit.ly/UNESCO2024
Venkatesh, V., ong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unied
theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
https://doi.org/10.2307/41410412
Zamith, R. (2021). Quantied audiences in news production: A synthesis and research agenda. Digital Journalism, 9(9), 1106–
1125.
https://doi.org/10.1080/21670811.2018.1444999
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>
<p>[Enlace de URL / hc (has AS)]</p>