Vivat Academia (2025).
ISSN: 1575-2844
|
Received: 10/03/2025 Accepted: 14/06/2025 Published: 12/07/2025 |
HOW ARTIFICIAL INTELLIGENCE CAN BE INTEGRATED INTO JOURNALISTIC PRACTICES
Enes Baloğlu[1]: Yozgat Bozok University. Türkiye.
Emrah Budak: Mersin University. Mersin.
How to cite the article:
Baloğlu, Enes & Budak, Emrah (2025). How artificial intelligence can be integrated into journalistic practices. Vivat Academia, 158, 1-21. https://doi.org/10.15178/va.2025.158.e1604
ABSTRACT
Introduction: Technological developments are constantly transforming many sectors. Artificial intelligence technologies are also leading to changes and transformations in journalistic practices. As AI technologies have made significant advances in the last few years, there is a growing need for journalism students to familiarize themselves with different AI technologies and adapt to such changes. Methodology: Semi-structured interviews were conducted with industry representatives and academics from Türkiye on which courses should be added in higher education in the process of using AI in journalism practices. Course proposals were developed from the main themes that emerged. Results: At the same time, workers in the media sector need to adopt AI technologies to adapt to communication technologies. Based on the results of interviews with industry experts and journalism academics and a literature analysis, this paper suggests that the curriculum of journalism departments should include courses on AI topics and applications. In this sense, it seems inevitable that both the representatives of the media sector and those preparing for the sector should be trained in artificial intelligence. Conclusion: The main conclusion of the study is that rather than artificial intelligence replacing human journalists, press sector employees and journalism students will increase productivity with artificial intelligence by receiving the necessary training.
Keywords: communication, journalism, artificial intelligence, journalism training, journalism curricula.
1. INTRODUCTION
The changes and transformations that artificial intelligence technology will bring about in journalistic practices are frequently discussed both by academic and industry experts. At the heart of the debates is how artificial intelligence will facilitate journalistic practices and what problems it will create in the media sector. Therefore, the process of adaptation of artificial intelligence to journalism and its multifaceted consequences after adaptation are important for the profession. As with the concept of big data, artificial intelligence includes supportive perspectives and criticisms (Broussard et al., 2019, p. 673). Artificial intelligence has the capacity to produce much more news than human journalists can prepare (Whittaker, 2019, p. 111). Artificial intelligence applications are seen as an added value of journalism in the digital age (Ali & Hassoun, 2019, p. 40) and are now considered advantageous in combating fake news (Biswal & Gouda, 2020, p. 161; Ali & Hassoun, 2019, p. 40) and personalizing content (Verma, 2024, p. 151; Biswal & Gouda, 2020, p. 160). Artificial intelligence is also seen as an important function in investigative journalism by making big data meaningful and presenting it to the reader and based on social benefit (Stray, 2021). In a study by Noain-Sánchez (2022, p. 105), it was found that artificial intelligence saves time, increases the efficiency of news-making processes, increases the efficiency of the media industry and improves the skills of journalists. For media outlets with limited staff and budgets, producing content on specific topics using automated tools with the help of AI points to a facilitating role (Kothari & Cruikshank, 2021, p. 20). AI has automated routine tasks while facilitating the production process of media content. In addition, collecting more data is a development that enables the discovery of insights into the media (Biswal & Gouda, 2020, p. 161).
On the other hand, AI can bring about a number of ethical issues in journalism, such as stifling creativity, laziness, transparency, data use and quality (Ali & Hassoun, 2019, p. 40). Productive AI applications have been hailed as “transformative”, “disruptive” and “a threat to human journalists and media professionals”. Much of this discourse reflects longstanding concerns about the impact of technological change on both the production and consumption ends of journalism (Matich et al., 2025). Journalism scholars note that automation is often favored by management for its cost-cutting potential, while journalists sensitive to the economic fragility of the news business often cite its inhuman and generic character as threatening to commodify news writing and render human labor redundant (Boyles & Meisinger, 2018; Moran & Shaikh, 2022; Pavlik, 2023). On the other hand, one of the important points is the concern about the presence of algorithmic bias in news generated by artificial intelligence (Kothari & Cruikshank, 2021, p. 21; Verma, 2024, p. 151). How the news is created should be transparently explained. Doing otherwise may undermine the integrity of journalism (Verma, 2024, p. 151). These ethical issues reveal the necessity to keep AI processes under constant control and supervision (Noain-Sánchez, 2022, p. 105).
The fact that artificial intelligence has such a positive and negative impact on journalism brings up the claim of transforming the profession (Ay, 2022, p. 917). Because artificial intelligence, which transforms the need for the use of human and technology required in journalism and the way of doing business to another dimension, also triggers the transformation of journalism. Indeed, considering that even the invention of the photocopy machine was a revolutionary development for journalism (Cohen et al., 2011, p. 67), the transformative power of artificial intelligence is an undeniable fact. In fact, Broussard et al. (2019, p. 673) argue that AI has transformed journalism in many aspects such as business models and work routines, emphasizing that regardless of the level of its transformative power, AI is part of a broader story about the structuring of journalism. Of course, we think that it would be the right approach to use this transformation in accordance with professional ethics, beneficial to society and with the potential to improve the profession. At this point, we put forward the idea that a human-centered perspective (Broussard et al., 2019, p. 674) should be developed on artificial intelligence and that the human role should not be ignored (Diakopoulos, 2019, p. 35). From this point of view, we seek the opinions of industry representatives and academics on which courses journalism students in higher education in Türkiye should take in order to comprehend artificial intelligence technologies on the basis of journalistic ethics, public interest and professional development. While studies on artificial intelligence in journalism often focus on the countries of the Global North (Thurman et al., 2017; Fanta, 2017; Brennen, 2018; Noain-Sánchez, 2022; Moran & Shaikh, 2022; Cools & Diakopoulos, 2024; González-Arias & López-García, 2024), this research on Türkiye will contribute to the use of artificial intelligence in Türkiye in a more accurate, efficient and needs-oriented manner in accordance with the journalistic culture. It is known that artificial intelligence applications are partially used in media organizations in Türkiye (Ay, 2023). However, there is a lack of research in the literature on what kind of training should be given to journalism students about integrating artificial intelligence into the transformation it brings to journalism. We propose an agenda to bring a determination and understanding of this issue for a proper integration into artificial intelligence. At this point, it is increasingly critical for teachers and academics to identify the ethical aspects of this technology, not only for current users, but also for potential technologists and developers who will create AI and IoT-enabled innovations in the near future (Jabar et al., 2024, p. 3). It is increasingly important to include AI technologies in higher education curricula in order to understand and use them correctly and effectively.
In a study conducted in Türkiye, a total of 59 communication faculties were examined and their course contents and curricula were analyzed. Within the scope of the research, it was investigated whether “the words ‘artificial intelligence’, ‘data science’, ‘data engineering’ and the words ‘metaverse’ and ‘virtual reality’, which are worlds related to artificial intelligence, are included in the course titles or content definitions of the courses” (Demirel, 2023, p. 9842). When the findings were analyzed on a departmental basis, it was seen that Media and Communication/New Media departments were more interested in artificial intelligence issues (Demirel, 2023, p. 9851). In this context, in this study, basic and elective course proposals including artificial intelligence were developed for the Journalism department. The research was conducted in Türkiye and expert interviews were conducted with experts in the sector.
1.1. Artificial Intelligence and Journalism
Communication technologies have always closely influenced the journalism industry. However, artificial intelligence (AI) is the latest technology that is causing disruptive changes in many areas across many industries and is increasingly being used to solve more complex challenges (Rahmanifard & Plaksina, 2019; Wang & Lu, 2019; Yang & Bang, 2019). The journalism industry has been leveraging AI since 2014 (Kim & Kim, 2021) and the journalism industry is, without exception, being transformed by AI (Broussard et al., 2019; Stray, 2019).
Generative AI can be defined as a technology that can create new content, including text, images, audio, video or other media, in response to training data and written prompts (Lorenz et al., 2023). The practice of journalism is undergoing transformations as automated AI processes enter different aspects of news production and distribution, especially in large-scale economies (Jamil, 2021). AI-enabled devices and software play an important role in the routine work of journalists, transforming the way news is produced and disseminated, and how people use or make sense of it (Bucher, 2017; Caswell & Dorr, 2018). AI-enabled developments are increasingly manifesting themselves in the news media industry, with many media outlets in major economies now using automation and AI-enabled devices and programs to generate and disseminate news (Marconi et al., 2017; Chou et al., 2021). Networks' deep machine learning algorithms are increasingly mining data without human supervision for structural patterns that form the “basis for new predictions” and decisions (Witten et al., 2017). Many major news organizations (such as the British Broadcasting Corporation, The Guardian, Forbes, The Washington Post, The Washington Post, The Los Angeles Times and The New York Times) and news agencies (such as Associated Press and Reuters) have now handed over weather, stock market movements, corporate performance and sports coverage to computers, recognizing that machines can be more rigorous and comprehensive than some reporters (Underwood, 2017). This shows that devices and machines are now taking on the role of communicators and replacing journalists (Jamil, 2021).
Journalism academics largely recognize the growing importance of AI and automation in newsrooms to help journalists sustain the ever-expanding scale of the global news media, while saving time and money and increasing speed and competence (Guzman, 2018; Lewis et al., 2019). AI software is also entering professional spaces as a tool that promises to streamline and automate certain types of writing, research, data analysis, computer vision and visual design (Thomson et al., 2024). The adoption of AI in the industry thus has numerous benefits, including improving the efficiency of journalists and competitiveness for media organizations (Broussard et al., 2019; Túñez-López et al., 2021). AI enhances newsrooms in many ways, such as tracking breaking news, crunching large data sets, generating leads, uncovering media insights, verifying news for fake news, and generally streamlining journalists' workflow (Hansen et al. 2017; Lewis et al., 2019). Furthermore, the use of algorithms to analyze data from various sources, translate text into audio and video, and determine sentiment are examples of how AI is being applied in newsrooms (Calvo Rubio & Ufarte Ruiz, 2021). AI-generated images are also routinely integrated into content published in news organizations, accompanying columns in the Australian tabloid newspaper The Daily Telegraph (Matich et al., 2025).
In textual news, more than half of the tech site CNET's fully AI-generated stories were found to contain factual errors (Sato & Roth, 2023), while Sports Illustrated published some with signatures of non-existent authors (Strauss & Sommer, 2023). We challenge claims that AI poses a unique misinformation threat, given the contingency of 'real' news images and the fact that humans equate realism with accuracy (Matich et al., 2025). On the other hand, while the emergence of AI is likely to exacerbate the problem of job displacement (Herrman, 2023) and employment issues related to journalistic labor, we argue that journalists who make the right use of AI can continue their profession. For this reason, we emphasize that there will always be a need for human journalists and that human journalists should keep up with the changes by receiving the necessary training. Then the question of how to build a hybrid journalism in which human and artificial intelligence act together (Diakopoulos, 2019) comes to mind. At this point, we believe that education is a priority in order to increase the literacy of journalists towards AI tools (Noain-Sánchez, 2022).
There are challenges facing the curriculum in the education of journalists (Deuze, 2001). Therefore, journalism curriculum frameworks should aim to provide students with different qualifications and journalism education is evolving towards interdisciplinarity (Bright, 2020). Journalism curricula need to be updated periodically, especially due to current developments in information and communication technologies (Arzuaga, 2022; García-Hernández, 2024; Okela, 2024). Kothari and Hickerson's research on journalism educators concluded that courses should be added to curricula to teach students automatic journalism practices (Kothari & Hickerson, 2020). Journalism programs are updating their curricula considering developments in the field of artificial intelligence (Wenger et al., 2025). At this point, there is a need to restructure artificial intelligence in journalism education in Türkiye to include literacy, practical competencies and ethical considerations (Şen, 2025).
In the near future, human and automated journalism will be integrated (Jamil, 2021). Artificial intelligence has emerged as an important and influential technology for journalism and media and is therefore of increasing importance for journalism and media education (Pavlik, 2023). Educators need to react quickly to these developments and make the necessary changes in curricula to prepare students for the industry. Only in this way will students be prepared for journalism practices that will continue with artificial intelligence. In this sense, it is important to ensure that students gain the necessary skills by focusing on the positive aspects of artificial intelligence technology.
2. OBJECTIVES
The aim of the study is to provide experts and academicians who teach in this field with course suggestions that they can add to the curriculum and to guide them in updating the education and training programs. With the new courses to be added to the curriculum, it is aimed to use artificial intelligence efficiently in journalism education and practices.
3. METHODOLOGY
Qualitative research aims to develop an in-depth understanding of the subject matter. In this context, the researcher acts as an explorer, tracing reality by asking additional questions and paying great attention to the subjective views of the participant. Therefore, despite the predefined questions, the semi-structured approach allowed the participants to develop key points and the interviewers to follow specific questions.
This research paper explores the following research questions:
RQ1: How does artificial intelligence affect Journalism practices and education?
RQ2: What are the compulsory and elective courses with AI content recommended by experts to be added to Journalism departments in Türkiye?
RQ3: What are the opinions of experts working in the Journalism sector in Türkiye regarding the use of artificial intelligence in their professions?
In-depth interviews were conducted with 13 experts (6 male and 7 female) working in Türkiye using purposive sampling method. Interviews were conducted face-to-face and through online platforms. The interviews ranged from a minimum of 38 minutes to a maximum of 76 minutes. The research questions were open-ended. For this reason, interviewees were given flexibility in responding. The selected male and female experts ranged in age from 25 to 65 and were working full-time in the media sector. The purpose of selecting both male and female experts was to ensure diversity of views and representation of both genders. Seven of the experts participating in this study were academics working in the press and six in journalism departments. To ensure the privacy and security of the research participants, all interviewed experts were numbered (ranging from 1 to 13). In addition, while discussing the results of the research, the findings of the studies in the literature are also included and similarities and differences are revealed. For this reason, the study benefits from the literature review for a comparative analysis of the results.
3.1. Sample and Data Collection
Purposive sampling method was utilized in this study. Purposive sampling is a sampling technique based on selecting individuals or groups that can provide the most information about the research topic for a specific purpose. Purposive sampling is especially preferred in cases where there is a limited universe or expertise is required on a particular topic (Palinkas et al., 2015). Our sampling criteria are that the interviewees are academics in the field of journalism and work in news organizations. Within the scope of the research, data were collected through face-to-face in-depth interviews between 27.05.2024 - 10.07.2024. A semi-structured questionnaire was used in the research. Within the scope of the research, a total of 7 experts and 6 academicians from the media sector were interviewed. The positions of the experts in the media sector are news editor, investigative journalist, social media manager, photojournalist, video editor and editor-in-chief.
4. RESULTS
Today, artificial intelligence (AI) technologies are significantly transforming Journalism departments in higher education (Pavlik, 2023). While AI enables efficiency and automation in these fields, it also requires students to acquire new skills as technology continues to evolve (Chen et al., 2020). In journalism practices, processes such as news gathering (Beckett & Yaseen, 2023), news production (Dörr, 2016; Molumby, 2020), news verification (Ali & Hassoun, 2019; Biswal & Gouda, 2020) and news distribution (Hagar & Diakopoulos, 2019; Lamot, 2021) are carried out with the support of artificial intelligence. This creates the need for students to develop strategies based on data analysis and learn how to use AI in creative processes. In the media sector, AI automates content production and enables faster delivery of news to selected target audiences using data analysis and news algorithms (Sánchez-García et al. 2023; Fieiras-Ceide et al. 2022). Consequently, this increases the necessity to teach data literacy and the ethical use of AI in journalism education. Therefore, it is increasingly important for students to learn how to use these technologies in their professional lives. As a result of all these developments, there is a need to design up-to-date curricula and educational models that include new technological developments (RQ1, RQ2, RQ3).
Within the scope of the research, 7 experts from the journalism sector and 6 journalism academics were interviewed. The professions of these experts are news editor, investigative journalist, social media manager, photojournalist, video editor and editorial director. The way experts use artificial intelligence in their professions and the courses that journalism academics recommend for the needs of the sector are as follows (RQ2, RQ3):
Table 1.
Journalism and Press Sector Experts Opinions and Course Suggestions
|
Expert |
Opinion |
Recommended Course |
Description of the Course |
|
News Editor (7) and Reporter (13) and Journalism Academician (1, 4) |
AI can create data-driven stories quickly, allowing editors to focus on in-depth investigations. |
Automated News Writing with Artificial Intelligence |
Teaches how to use AI-based automated news writing programs. |
|
Investigative Journalist (8) and Journalism Academician (2, 3) |
AI quickly uncovers insights and important connections through big data analysis. |
Big Data Analytics and Artificial Intelligence in Journalism |
It teaches how to analyze large data sets and uncover important insights. |
|
Social Media Manager (9) and Journalism Academician (3, 5) |
AI enables interaction and content optimization with social media analytics and chatbots. |
Social Media and Artificial Intelligence Analytics |
It teaches how to analyze and optimize the performance of social media content. |
|
Photo journalist (10) Journalism Academician (2, 6) |
AI checks for ethical use and accuracy through photo analysis and editing. |
Image Recognition and Artificial Intelligence |
Teaches the use of artificial intelligence in photo analysis and editing processes. |
|
Video Editor (11) and Journalism Academician (1, 3) |
AI improves efficiency in video editing with automated editing and translation technologies. |
Video Editing with Artificial Intelligence |
Teaches AI-based video editing software and techniques. |
|
Editorial Director (12)and Journalism Academician (4) |
AI optimizes content planning and distribution by analyzing reader trends. |
Broadcast Management with Artificial Intelligence |
Teaches how to optimize content planning and distribution by analyzing reader trends. |
Source: Own elaboration.
According to Table 1, the artificial intelligence courses recommended by experts for the journalism profession are thought to make significant contributions to those working in various fields. For example, for news editors, artificial intelligence enables editors to focus on in-depth research by enabling the rapid creation of data-based news. Investigative journalists can quickly uncover important information and connections through big data analysis. Social media managers can optimize content by interacting with AI-powered analytics and chatbots. Photojournalists can analyze photos with AI to check for ethical use and accuracy. Marconi (2020) states that image recognition technology with the help of artificial intelligence provides the opportunity to capture details that the human eye cannot see. In addition, image recognition technology reveals the source of the video, detecting whether there is any intervention in the video and preventing possible manipulation (de-Lima-Santos & Ceron, 2022; Herrera, 2023). In the newsroom, image recognition technology can speed up the production and editing of photos and videos and provide a holistic perspective on the news story with the data obtained from visual clues. Moreover, photos and videos are automatically labeled in real time. It allows for quick discovery of desired scenes and moments and text-to-visual matching. Comcast NBCUniversal employs staff to develop image recognition algorithms, and CBS Interactive uses image recognition technology to improve content recommendations and automatically add data to videos. The Las Vegas Review-Journal and USA Today use video automation systems in their content (Marconi, 2020).
Artificial intelligence algorithms can transform complex data into meaningful and engaging infographics, graphics and visualizations (Wu et al., 2022). In this context, Deutsche Welle announced that it could use generative AI to “create or improve illustrations or data visualizations” (Kasper-Claridge, 2023, para. 14). Publications such as The New York Times feature AI-generated images and label them as AI-generated (see Figure 1).
Figure 1.
Generative visual AI in the wild
Source: Produced by the New York Times using artificial intelligence
News writing with the help of artificial intelligence brings convenience for journalists. Automated news writing has advantages such as efficiency and cost reduction (Li, 2024). Automated news production (Graefe, 2016) based on making sense of digital traces and data (Tandoc & Thomas, 2015) makes journalists appreciate the advantages of algorithms such as improved efficiency and data processing capabilities (Cools & Diakopoulos, 2024). Considering that while automated news writing contributes to journalism, it does not completely eliminate the role of the journalist (Kurnianti et al., 2024), machine-human integration gains importance. For this reason, there is a need for artificial intelligence training for journalists on automated news writing that both facilitates journalism and adheres to ethical principles. Wang et al. (2021) state that in parallel with the development of artificial intelligence, journalists should play a dominant role, be supervisors, and that journalism can become a more productive profession by realizing the right computer-human cooperation. When man and machine are in deep cooperation, journalism can progress (Li, 2024). In addition, editors can optimize content planning and distribution more accurately by analyzing reader trends with AI. These contributions help journalists to work more efficiently, increase accuracy, speed and interactivity in journalism, and provide readers with more quality and reliable content.
Social media analytics offers many advantages to today's media organizations. According to Tejedor and Vila (2021), media organizations such as AP, Reuters, Forbes, Los Angeles Times identify breaking news on social networks and comments by searching keywords with artificial intelligence applications. In addition, a wide flow of information can be created by categorizing, characterizing and grouping the collected data. Lynx Insight, the artificial intelligence tool used by Reuters for analysis, scans large amounts of information such as financial reports and social media trends to identify newsworthy agendas and provides a valuable resource for news discovery and trends (Amponsah & Atianashie, 2024). In addition, this provides journalists with the opportunity to extract news that is more resonant and interesting in society (Gül, 2024).
5. DISCUSSION
Today, the rapid development of artificial intelligence necessitates its integration into many professions. Therefore, it is necessary for both press representatives working in the sector and journalism students who are trained in this field to receive artificial intelligence training. In this sense, experts and academics in the field recommend the training and courses that both press representatives and journalism students should take. Based on expert opinions and literature, we emphasize the need for press representatives and journalism students to keep themselves up-to-date. Restructuring the education curricula to cover topics such as AI-supported content production, algorithm-based data analysis, automated news writing, digital media management, and new publishing techniques in line with the needs of the age will increase the competitiveness of graduates in the sector.
Artificial Intelligence (AI) is becoming increasingly integrated into the field of journalism, transforming various aspects of news production, distribution and consumption. The possibilities offered by this technology are increasingly used in media organizations, from algorithms that help suggest the most relevant content for users, to tools that detect news and automate some of the tasks of journalists.
AI automates routine tasks in journalism such as data collection, content production and distribution, allowing journalists to focus on more complex and creative tasks (Peña-Fernández et al. 2023; Ahmad et al., 2023). This integration of AI into the journalism profession is changing the professional roles of journalists, reducing the need for routine tasks and increasing the demand for cognitive and creative contributions (Túñez-López et al., 2021). AI tools are actively used in media and journalism organizations for writing, editing, production and distribution of various media content. However, this also raises concerns about deepfake news, news bias, dismissal and insufficient transparency (Aissani et al., 2023).
Therefore, constant oversight and ethical guidelines are needed to manage the effects of AI in news production. Another consequence is the use of automated systems to produce news content without human intervention, leading to the emergence of “synthetic media” powered by algorithms (Ufarte-Ruiz et al., 2023). Within the scope of these developments, it is important for students studying journalism to learn courses on artificial intelligence added to the curriculum in order to master current developments. In this context, as a result of the sector interviews and literature review, the compulsory and elective courses recommended for journalism departments for 8 semesters are as shown in Table 2.
Table 2.
Recommended Courses to be Added to the Curriculum
|
Class 1 |
||
|
Course Type |
Course Name |
Content Summary |
|
Mandatory |
Fundamentals of Artificial Intelligence |
Basic principles, history and basic algorithms of artificial intelligence. |
|
Selective1 |
Prompt Authorship |
Teaching how to create the right content with artificial intelligence. |
|
Selective2 |
Artificial Intelligence Literacy |
Gains the ability to distinguish between the right and wrong content produced by AI. |
|
Class 2 |
||
|
Course Type |
Course Name |
Content Summary |
|
Mandatory |
Big Data Analytics and Artificial Intelligence in Journalism |
It teaches big data analysis and the use of artificial intelligence in journalism. |
|
Selective |
AI and Media Technologies |
It teaches how artificial intelligence technologies are used in journalism processes and enables students to use AI effectively in the media industry. |
|
Class 3 |
||
|
Course Type |
Course Name |
Content Summary |
|
Mandatory |
AI and Ethics |
Ethical, privacy and legal issues in artificial intelligence applications. |
|
Selective |
Social Media and Artificial Intelligence Analytics |
It teaches how to analyze and optimize the performance of social media content. |
|
Class 4 |
||
|
Course Type |
Course Name |
Content Summary |
|
Mandatory |
Content Production with AI in Journalism |
This course teaches how AI can personalize media content and focuses specifically on personalized content strategies for journalism. |
|
Selective |
Content management with Artificial intelligence in journalism |
The use of news broadcasting with AI teaches the planning and production of different types of programs. It teaches how to optimize content planning and distribution by analyzing reader trends. |
Source: Own elaboration.
As AI continues to shape and redefine fundamental aspects of journalism, an ethical perspective becomes imperative to assess the impact on journalistic principles, integrity and the broader societal impacts of information dissemination (Paik, 2023), as AI systems carry ethical risks such as bias, unfairness and lack of transparency in data processing, decision-making and autonomous applications (Steinke et al., 2022). Therefore, newsrooms need to support staff professional development with training on how algorithms work and how productive AI works to ensure that they use AI responsibly and ethically (Thomson et al., 2024; Cools & Diakopoulos, 2024). After training, newsroom managers should create or develop formal newsroom policies on exactly how/when/why AI is (or is not) appropriate for journalistic use. This representation should include model training, privacy and compliance (Matich et al., 2025). In this way, the boundaries of ethical and effective use of AI can be set and it can act as an adjunct rather than a replacement for human journalists.
The AI literacy course prepares individuals to question AI-generated content, critically question its sources and verify its authenticity (Cools & Diakopoulos, 2024). Already, the widespread production of sports and financial news by artificial intelligence (Thurman, 2018; Backus et al., 2023) leads us to the necessity of Content Production with AI in Journalism course. This will give journalists the time to focus on more complex stories that require in-depth analysis and human insight (Frackiewicz, 2025), such as slow journalism (Le Masurier, 2015), investigative journalism (Glasser & Ettema, 1989).
The Prompt Authorship course provides the ability to create new content such as text, images, audio, video or other media based on individuals' written prompts (Diakopoulos et al. 2024; Simon, 2024). Especially after easy access to information and data through prompts created by artificial intelligence tools such as ChatGPT, the availability of many tools to summarize the text makes story writing easier. Gerard Lipscombe (2023) states that ChatCPT has developed many creative prompts for journalists and emphasizes that it facilitates many issues such as generating article ideas, preparing interview questions, reporting current events, data journalism techniques, and storytelling methods in social media.
There is a need to increase the literacy levels of journalists on artificial intelligence. In order to not only have knowledge about artificial intelligence, but also to think about its impact and agency dimension, to consider its normative aspect, and to use it creatively and efficiently (Deuze & Becketti, 2022), the level of literacy needs to increase. At this point, AI literacy requires accepting that technology is a reality of journalism and using technology in a way that is appropriate for the benefit of society and journalism professional ethics.
With Big Data Analytics and Artificial Intelligence in Journalism, journalists learn how to analyze big data and use artificial intelligence in journalism. In general, news writing becomes more practical when analyzing large documents or image sets (Simon, 2024). In this context, AI tools used as a supporting component to help journalists become more skilled and to improve specific news production process activities facilitate and raise journalists' working standards (Tejedor & Vila, 2021).
A more collaborative future between AI and journalists is proposed, focusing on increasing efficiency rather than replacing each other (Lewis et al., 2019). In order to effectively integrate AI tools into journalistic routines, the need for appropriate training to bridge the knowledge gap is emphasized (Gutierrez Lopez et al., 2023; Cai & Nishal, 2023; Eskiadi & Panagiotou, 2025). As a result, press representatives and journalism students should keep up with technological developments by receiving the necessary training (Kazmi & Ali, 2025). In this sense, press representatives and journalism students should gain skills to establish a balance between developing technology and human beings.
The study was conducted in Türkiye with 13 participants. In this respect, the findings are limited to Türkiye. First of all, the curriculum proposal is aimed at higher education institutions in Türkiye that provide education in the field of journalism. In future studies, conducting more comprehensive studies on sector experts, academics, students and readers can ensure a stronger integration of artificial intelligence into higher education programmes.
6. REFERENCES
Ahmad, N., Haque, S., & Ibahrine, M. (2023). The News Ecosystem in the Age of AI: Evidence from the UAE. Journal of Broadcasting & Electronic Media, 67(3), 323-352. https://doi.org/10.1080/08838151.2023.2173197
Aissani, R., Abdallah, R. A. Q., Taha, S., & Al Adwan, M. N. (2023). Artificial Intelligence Tools in media and journalism: Roles and concerns. In M. Alsmirat, Y. Jararweh, J. Lloret & M. Aloqaily (Eds.), 2023 International Conference on Multimedia Computing, Networking and Applications (MCNA) (pp. 19-26). IEEE.
Ali, W., & Hassoun, M. (2019). Artificial intelligence and automated journalism: Contemporary challenges and new opportunities. International Journal of Media, Journalism and Mass Communications, 5(1), 40-49. http://dx.doi.org/10.20431/2454-9479.0501004
Amponsah, P. N., & Atianashie, A. M. (2024). Navigating the New Frontier: A Comprehensive Review of AI in Journalism. Advances in Journalism and Communication, 12, 1-17. https://doi.org/10.4236/ajc.2024.121001
Arzuaga, L. (2022). From the classroom to the newsroom: A critical route to introduce AI in journalism education. Facts & Frictions, 2(1), 80-97. https://acortar.link/nTwMmT
Ay, A. (2022). Yapay Zekâ Haberciliği ve Gazetecilik Tartişmalarina Dair Bir Değerlendirme. The Turkish Online Journal of Design Art and Communication, 12(4), 913-926. https://dergipark.org.tr/tr/pub/tojdac/issue/72513/1143688
Ay, A. (2023). Gazetecilik 4.0: Yapay Zekâ Haberciliğinin Güncel Örneklerine Yönelik Bir İnceleme. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 14(38), 412-435. https://doi.org/10.21076/vizyoner.1141093
Backus, B., Bloom, B., Lazzeri, A., Yordanov, Y., & Freund, Y. (2023). The evolution of academic publishing: Mega journals and artificial intelligence-authored articles. European Journal of Emergency Medicine, 30(3), 151-152. https://doi.org/10.1097/mej.0000000000001036
Beckett, C., & Yaseen, M. (2023). Generating Change. A global survey of what news organisations are doing with AI. The London School of Economics and Political Science. https://bit.ly/4eC3ljV
Biswal, S. K., & Gouda, N. K. (2020). Artificial Intelligence in Journalism: A Boon or Bane?. In A. Kulkarni y S. Satapathy (Eds.), Optimization in Machine Learning and Applications. Algorithms for Intelligent Systems. Springer. https://doi.org/10.1007/978-981-15-0994-0_10
Boyles, J. L., & Meisinger, J. (2018). Automation and Adaptation: Reshaping Journalistic Labor in the Newsroom Library. Convergence, 26(1), 178-192. https://doi.org/10.1177/1354856518772542
Brennen, J. (2018). An industry-led debate: How UK media cover artificial intelligence. Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/our-research/industry-led-debate-how-uk-media-cover-artificial-intelligence
Bright, A. (2020). Journalism curriculum frameworks shift toward skills, interdisciplinarity. Teaching Journalism & Mass Communication, 10(1), 1-7. https://aejmc.us/spig/wp-content/uploads/sites/9/2020/06/TJMC-10.1-Bright-Journalism-Curriculum-Frameworks.pdf
Broussard, M., Diakopoulos, N., Guzman, A. L., Abebe, R., Dupagne, M., & Chuan, C. H. (2019). Artificial intelligence and journalism. Journalism & Mass Communication Quarterly, 96(3), 673-695. https://doi.org/10.1177/1077699019859901
Bucher, T. (2017). ‘Machines don’t have instincts’: Articulating the computational in journalism. New Media & Society, 19(6), 918-933. https://doi.org/10.1177/1461444815624182
Cai, M., & Nishal, S. (2023). Motivations, Goals, and Pathways for AI Literacy for Journalism. In CHI’23 Workshop on AI Literacy: Finding Common Threads between Education, Design, Policy, and Explainability. https://nishalsach.github.io/pdfs/2023-ai-literacy-chi.pdf
Calvo-Rubio, L. M., & Ufarte-Ruiz, M. J. (2021). Artificial intelligence and journalism: Systematic review of scientific production in web of science and scopus (2008-2019). Communication and Society, 34(2), 159-176. https://doi.org/10.15581/003.34.2.159-176
Caswell, D., & Konstantin, D. (2018). Automated Journalism 2.0: Event-Driven Narratives. Journalism Practice, 12(4), 477-496. https://doi.org/10.1080/17512786.2017.1320773
Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
Chou, K. C., Liao, C. S., & Chiang, T. H. C. (2021). The application and development of artificial intelligence in taiwan’s news communication. Taiwan Journal of East Asian Studies, 18(1), 43-88. https://doi.org/10.6163/TJEAS.202106_18(1).0002
Cohen, S., Hamilton, J. T., & Turner, F. (2011). Computational Journalism. Communications of the ACM, 54(10), 66-71, https://doi.org/10.1145/2001269.2001288
Cools, H., & Diakopoulos, N. (2024). Uses of generative AI in the newsroom: Mapping journalists’ perceptions of perils and possibilities. Journalism Practice, 1-19. https://doi.org/10.1080/17512786.2024.2394558
de-Lima-Santos, Mathias-Felipe, & Wilson Ceron. (2022). Artificial Intelligence in News Media: Current Perceptions and Future Outlook. Journalism and Media, 3(1), 13-26. https://doi.org/10.3390/journalmedia3010002
Demirel, E. S. (2023). İletişim Alanında Yapay Zekâ: İletişim Fakültelerinde Yapay Zekâ Çalışmaları Üzerine Bir Araştırma. International Social Sciences Studies Journal, 9(118), 9842-9852. http://dx.doi.org/10.29228/sssj.74032
Deuze, M. (2001). Educating ‘new’journalists: Challenges to the curriculum. Journalism & Mass Communication Educator, 56(1), 4-17. https://doi.org/10.1177/107769580105600102
Deuze, M., & Beckett, C. (2022). Imagination, Algorithms and News: Developing AI Literacy for Journalism. Digital Journalism, 10(10), 1913-1918. https://doi.org/10.1080/21670811.2022.2119152
Diakopoulos, N. (2019). Automating the news: How algorithms are rewriting the media. Harvard University Press. http://www.jstor.org/stable/j.ctv24w634d
Dörr, K. N. (2016). Mapping the Field of Algorithmic Journalism. Digital Journalism, 4(6), 700-722. https://doi.org/10.1080/21670811.2015.1096748
Eskiadi, I., & Panagiotou, N. (2025). AI in Communication and News Media Organizations: Current Transformations and Future Directions. Envisioning the Future of Communication, 2(1), 224-236. https://eproceedings.epublishing.ekt.gr/index.php/EFoC/article/view/7910
Fanta, A. (2017). Putting Europe’s robots on the map: Automated journalism in news agencies. University of Oxford; Reuters Institute for the Study of Journalism. https://bit.ly/44hTwUI
Fieiras-Ceide, C., Vaz-Álvarez, M., & Túñez-López, M. (2022). Artificial intelligence strategies in European public broadcasters: Uses, forecasts and future challenges. Profesional De La información, 31(5). https://doi.org/10.3145/epi.2022.sep.18
Frackiewicz, M. (2025, July 7). The AI Video Revolution: How Generators Turn Text into Hollywood-Ready Clips. TS2. https://ts2.tech/en/the-ai-video-revolution-how-generators-turn-text-into-hollywood-ready-clips/
García-Hernández, C. (2024). Journalism as an articulator of knowledge. A proposal of curricular design in the professional training of communicators. In INTED2024 (pp. 1338-1345). IATED. https://doi.org/10.21125/inted.2024.0403
Glasser, T. L., & Ettema, J. S. (1989). Investigative journalism and the moral order. Critical Studies in Media Communication, 6(1), 1-20. https://doi.org/10.1080/15295038909366728
González-Arias, C., & López-García, X. (2024). Rethinking the Relation between Media and Their Audience: The Discursive Construction of the Risk of Artificial Intelligence in the Press of Belgium, France, Portugal, and Spain. Journalism and Media, 5(3), 1023-1037. https://doi.org/10.3390/journalmedia5030065
Graefe, A. (2016, January 7). Guide to automated journalism. Tow Center for Digital Journalism. https://www.cjr.org/tow_center_reports/guide_to_automated_journalism.php
Gül, K. (2024). Yapay Zeka Teknolojilerinin Gazetecilik Alanında Kullanımı: Avantajlar ve Dezavantajları. İletişim ve Toplum Araştırmaları Dergisi, 4(2), 306-331. https://doi.org/10.59534/jcss.1457410
Gutierrez Lopez, M., Porlezza, C., Cooper, G., Makri, S., MacFarlane, A., & Missaoui, S. (2023). A question of design: Strategies for embedding AI-driven tools into journalistic work routines. Digital Journalism, 11(3), 484-503. https://doi.org/10.1080/21670811.2022.2043759
Hagar, N., & Diakopoulos, N. (2019). Optimizing Content with A/B Headline Testing: Changing Newsroom Practices. Media and Communication, 7(1), 117-127. https://www.cogitatiopress.com/mediaandcommunication/article/view/1801
Herrera, A. (2023, May 22). How Journalists Can Detect Altered Images and AI-Generated Content. Foreign Press Correspondents USA. https://foreignpress.org/journalism-resources/how-journalists-can-detect-altered-images-and-ai-generated-content
Herrman, J. (2023, August 1). How Will Artificial Intelligence Change the News Business? New York. https://nymag.com/intelligencer/2023/08/how-ai-will-change-the-news-business.html
Jabar, M., Chiong-Javier, E., & Pradubmook Sherer, P. (2024). Qualitative ethical technology assessment of artificial intelligence (AI) and the internet of things (IoT) among filipino Gen Z members: implications for ethics education in higher learning institutions. Asia Pacific Journal of Education, 1-15. https://doi.org/10.1080/02188791.2024.2303048
Jamil, S. (2021). Artificial Intelligence and Journalistic Practice: The Crossroads of Obstacles and Opportunities for the Pakistani Journalists. Journalism Practice, 15(10), 1400-1422. https://doi.org/10.1080/17512786.2020.1788412
Kasper-Claridge, M. (2023, September 19). What is Deutsche Welle’s Approach to Generative AI?. Deutsche Welle. https://www.dw.com/en/what-is-deutsche-welles-approach-to-generative-ai/a-66868035
Kazmi, S. H. Z., & Ali, M. (2025). The Role of AI in Journalism: Perspectives from Media Professionals. Review Journal of Social Psychology & Social Works, 3(1), 92-99. https://doi.org/10.71145/risp.v3i1.79
Kim, D., & Kim, S. (2021). A model for user acceptance of robot journalism: Influence of positive disconfirmation and uncertainty avoidance. Technological Forecasting and Social Change, 163, 120448. https://doi.org/10.1016/j.techfore.2020.120448
Kothari, A., & Cruikshank, S. A. (2021). Artificial Intelligence and Journalism: An Agenda for Journalism Research in Africa. African Journalism Studies, 43(1), 17-33. https://doi.org/10.1080/23743670.2021.1999840
Kothari, A., & Hickerson, A. (2020). Challenges for journalism education in the era of automation. Media Practice and Education, 21(3), 212-228. https://doi.org/10.1080/25741136.2020.1787724
Kurnianti, I., Hadiprashada, D., Muzni, N., & Ceesay, S. (2024). The Phenomenon of Artificial Intelligence Usage in News Writing Styles by Journalists of Bengkulu Ekspress. com. Komunikator, 16(2), 249-261. https://doi.org/10.18196/jkm.24573
Lamot, K. (2021). Metrics for News: The Uses and Effects of Analytics in Journalism (Doctoral dissertation). University of Antwerp. https://repository.uantwerpen.be/desktop/irua
Le Masurier, M. (2015). What is slow journalism?. Journalism Practice, 9(2), 138-152. https://doi.org/10.1080/17512786.2014.916471
Lewis, S. C., Guzman, A. L., & Schmidt, T. R. (2019). Automation, journalism, and human–machine communication: Rethinking roles and relationships of humans and machines in news. Digital Journalism, 7(4), 409-427. https://doi.org/10.1080/21670811.2019.1577147
Li, Y. (2024). Machine news writing in the age of artificial Intelligence: Current situation and future prospects. In 2024 10th International Conference on Humanities and Social Science Research (ICHSSR 2024) (pp. 1147-1154). Atlantis Press. https://doi.org/10.2991/978-2-38476-277-4_126
Lipscombe, G. (2023, December 6). 11 Creative ChatGPT Prompts for Journalists (December 2023). Bizway. https://www.bizway.io/blog/chatgpt-prompts-for-journalists
Lorenz, P., Perset, K., & Berryhill, J. (2023). Initial Policy Considerations for Generative ArtificialIntelligence. OECD Artificial Intelligence Papers, 1. https://doi.org/10.1787/fae2d1e6-en
Marconi, F. (2020). Newsmakers: Artificial intelligence and the future of journalism. Columbia University Press.
Marconi, F., Siegman, A., & Journalist, M. (2017). The future of augmented journalism: A guide for newsrooms in the age of smart machines. AssociatedPress. https://jeanetteabrahamsen.com/wp-content/uploads/2017/09/ap_insights_the_future_of_augmented_journalism.pdf
Matich, P., Thomson, T. J., & Thomas, R. J. (2025). Old Threats, New Name? Generative AI and Visual Journalism. Journalism Practice, 1-20. https://doi.org/10.1080/17512786.2025.2451677
Molumby, C. (2020, November 24). Modus, Semi-Automatically Creating New Story Modes. BBC News Labs. https://www.bbc.co.uk/rdnewslabs/projects/modus
Moran, R. E., & Shaikh, S. J. (2022). Robots in the News and Newsrooms: Unpacking Metajournalistic Discourse on the Use of Artificial Intelligence in Journalism. Digital Journalism, 10(10), 1756-1774. https://doi.org/10.1080/21670811.2022.2085129
Noain-Sánchez, A. (2022). Addressing the impact of artificial intelligence on journalism: The perception of experts, journalists and academics. Communication & Society, 35(3), 105-121. https://doi.org/10.15581/003.35.3.105-121
Okela, A. H. (2024). Artificial Intelligence and the Future of Journalism Education: Opportunities and Challenges in Egypt. Journalism & Mass Communication Educator, 80(1), 54-69. https://doi.org/10.1177/10776958241296502
Paik, S. (2023). Journalism Ethics for the Algorithmic Era. Digital Journalism, 13(4), 1-27. https://doi.org/10.1080/21670811.2023.2200195
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and policy in mental health and mental health services research, 42, 533-544. https://doi.org/10.1007/s10488-013-0528-y
Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), 84-93. https://doi.org/10.1177/10776958221149577
Peña-Fernández, S., Meso-Ayerdi, K., Larrondo-Ureta, A., & Díaz-Noci, J. (2023). Without journalists, there is no journalism: the social dimension of generative artificial intelligence in the media. Profesional De La información, 32(2). https://doi.org/10.3145/epi.2023.mar.27
Rahmanifard, H., & Plaksina, T. (2019). Application of Artificial Intelligence Techniques in the Petroleum Industry: A Review. Artificial Intelligence Review, 52(4), 2295-2318. https://doi.org/10.1007/s10462-018-9612-8
Sánchez-García, P., Merayo Álvarez, N., Calvo Barbero, C., & Diez-Gracia, A. (2023). Spanish technological development of artificial intelligence applied to journalism: companies and tools for documentation, production and distribution of information. Profesional De La información, 32(2). https://doi.org/10.3145/epi.2023.mar.08
Sato, M., & Roth, E. (2023, Jan 25). CNET Found Errors in More Than Half of its AI-Written Stories. The Verge. https://www.theverge.com/2023/1/25/23571082/cnet-ai-written-stories-errors-corrections-red-ventures
Şen, A. F. (2025). Journalism Education in the Age of AI: Curriculum Analysis from Turkey. Advances in Applied Sociology, 15(4), 311-330. https://doi.org/10.4236/aasoci.2025.154017
Simon, F. M. (2024). Escape me if you can: How ai reshapes news organisations’ dependency on platform companies. Digital Journalism, 12(2), 149-170. https://doi.org/10.1080/21670811.2023.2287464
Steinke, G., LaBrie, R., & Sarkar, S. (2022). Recommendation for Continuous Ethical Analysis of AI Algorithms. In D. Megías & R. Di Pietro (Eds.), Proceedings of the 2022 European Interdisciplinary Cybersecurity Conference (EICC '22) (pp. 104-106). Association for Computing Machinery. https://doi.org/10.1145/3528580.3532996
Strauss, B., & Sommer, W. (2023). Sports Illustrated’s Use of AI infuriates a Staff Already in Turmoil. The Washington Post. https://www.washingtonpost.com/sports/2023/11/28/sports-illustrated-ai-articles/
Stray, J. (2019). Making Artificial Intelligence Work for Investigative Journalism. Digital Journalism, 7(8), 1076-1097. https://doi.org/10.1080/21670811.2019.1630289
Stray, J. (2021). Making artificial intelligence work for investigative journalism. En N. T. Seth, C. Lewis & J. Kunert (Eds.), Algorithms, Automation, and News (pp. 97-118). Routledge. https://bit.ly/44LDZMV
Tandoc Jr, E. C, & Thomas, R. J. (2015). The ethics of web analytics: Implications of using audience metrics in news construction. Digital Journalism, 3(2), 243-258. https://doi.org/10.1080/21670811.2014.909122
Tejedor, S., & Vila, P. (2021). Exo Journalism: A Conceptual Approach to a Hybrid Formula between Journalism and Artificial Intelligence. Journalism and media, 2(4), 830-840. https://doi.org/10.3390/journalmedia2040048
Thomson, T. J., Thomas, R. J., & Matich, P. (2024). Generative Visual AI in News Organizations: Challenges, Opportunities, Perceptions, and Policies. Digital Journalism, 1-22. https://doi.org/10.1080/21670811.2024.2331769
Thurman, N. J. (2018). Computational Journalism. In K. Wahl-Jorgensen & T. Hanitzsch (Eds.), The Handbook of Journalism Studies (2nd ed., pp. 180-195). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781315167497-12/computational-journalism-neil-thurman
Thurman, N., Dörr, K., & Kunert, J. (2017). When reporters get hands-on with robo-writing: Professionals consider automated journalism’s capabilities and consequences. Digital Journalism, 5(10), 1240-1259. https://doi.org/10.1080/21670811.2017.1289819
Túñez-López, J. M., Fieiras-Ceide, C., & Vaz-Álvarez, M. (2021). Impact of Artificial Intelligence on Journalism: transformations in the company, products, contents and Professional profile. Communication & Society, 34(1), 177-193. https://doi.org/10.15581/003.34.1.177-193
Ufarte-Ruiz, M., Murcia-Verdú, F., & Túñez-López, J. (2023). Use of artificial intelligence in synthetic media: First newsrooms without journalists. Profesional De La información, 32(2). https://doi.org/10.3145/epi.2023.mar.03
Underwood, C. (2017, June 22). Automated Journalism – AI Applications at New York Times, Reuters, and Other Media Giants. Emerj. https://emerj.com/ai-sector-overviews/automated-journalism-applications/
Verma, D. (2024). Impact of Artificial Intelligence on Journalism: A Comprehensive Review of AI in Journalism. Journal of Communication and Management, 3(02), 150-156. https://doi.org/10.58966/JCM20243212
Wang, Q., & Lu, P. (2019). Research on Application of Artificial Intelligence in Computer Network Technology. International Journal of Pattern Recognition and Artificial Intelligence, 33(5), 1959015. https://doi.org/10.1142/S0218001419590158
Wang, X., Chen, Z., & Yan, L. (2021). Obstacles and Proposals of Artificial Intelligence News Writing. International Core Journal of Engineering, 7(12), 139-146. https://www.icj-e.org/download/ICJE-7-12-139-146.pdf
Wenger, D., Hossain, M. S., & Senseman, J. R. (2025). AI and the Impact on Journalism Education. Journalism & Mass Communication Educator, 80(1), 97-114. https://doi.org/10.1177/10776958241296497
Whittaker, J. (2019). Tech Giants, Artificial Intelligence, and the Future of Journalism. Routledge.
Witten, I. H., E. Frank, M. A. Hall, & Pal, C. J. (2017). What’s It All About? In I. H. Witten, E. Frank, M. A. Hall & C. J. Pal (Eds.), Data Mining: Practical Machine Learning Tools and Techniques (4th ed., pp. 3-41). Elsevier. https://doi.org/10.1016/B978-0-12-804291-5.00001-5
Wu, A., Wang, Y., Shu, X., Moritz, D., Cui, W., Zhang, H., Zhang, D., & Qu, H. (2022). Ai4vis: Survey on artificial intelligence approaches for data visualization. IEEE Transactions on Visualization and Computer Graphics, 28(12), 5049-5070. https://doi.ieeecomputersociety.org/10.1109/TVCG.2021.3099002
Yang, Y. J., & Bang, C. S. (2019). Application of Artificial Intelligence in Gastroenterology. World Journal of Gastroenterology, 25(14), 1666-1683. https://doi.org/10.3748/wjg.v25.i14.1666
AUTHORS' CONTRIBUTIONS, FINANCING AND ACKNOWLEDGMENTS
Contributions of the authors:
Conceptualization: Enes Baloğlu. Methodology: Enes Baloğlu. Software: Emrah Budak. Validation: Emrah Budak. Formal Analysis: Enes Baloğlu. Data curation: Emrah Budak. Writing-Preparation of the original draft: Enes Baloğlu. Drafting-Revision and Editing: Enes Baloğlu. Visualization: Emrah Budak. Supervision: Emrah Budak. Project Administration: This study does not include any project. All Authors have read and accepted the published version of the manuscript: Enes Baloğlu y Emrah Budak.
Funding: This research has no external funding.
Conflict of interests: No conflict of interest.
AUTHOR/S :
Enes Baloğlu: He is an associate professor in the Department of New Media and Communication of Yozgat Bozok University. His academic interests include topics such as sports journalism, women’s sports journalist and online harassment, social media and journalism, the relationship between sports and hegemony.
Orcid ID: https://orcid.org/0009-0001-1960-1688
Emrah Budak: He is an associate professor in the Department of Journalism of Mersin University. His academic interests include topics such as constructive journalism, drone journalism and podcast journalism, social media and journalism.
Orcid ID: https://orcid.org/0000-0001-6896-7558