Impacts of artificial intelligence (AI) on journalism education

1. Changes in journalistic practice and trends in integrating AI into professional journalism education

1.1. Changes in journalistic practice

Artificial intelligence (AI) has deeply penetrated the digital transformation processes of newsrooms. This development is no longer merely a trend but has increasingly become a standard mode of operation. The JournalismAI Global Report affirms that “artificial intelligence is no longer an experiment in newsrooms but is becoming part of the standard operational infrastructure of the journalism industry” (JournalismAI, 2024, p. 7). Similarly, the Digital News Report published by the Reuters Institute notes that AI is involved throughout the entire news production chain, from data collection and content creation to distribution: “AI tools are increasingly integrated across the whole news production process, including data gathering, automated content creation, verification, audience analysis, and content distribution” (Reuters, 2024, p. 36).

These developments indicate that AI does not merely support isolated technical tasks but is reshaping the overall operational model of newsrooms in today’s digital journalism environment. Such applications are not confined to large media conglomerates. Even small newsrooms with fewer than five journalists can deploy AI to automate routine tasks, including large-scale data collection, automated drafting, fact-checking, sentiment analysis, and content distribution to target audiences. This process has accelerated editorial workflows and enhanced internal training, enabling newsrooms to operate more efficiently. At the same time, it has created new pressures on journalism education institutions to better prepare students for integration into AI-augmented newsroom environments.

Alongside these opportunities, AI also raises significant ethical and legal challenges. According to UNESCO (2023, pp. 9-12), the principal ethical risks of AI in journalism include: (i) algorithmic bias; (ii) violations of privacy; (iii) copyright infringement; and (iv) the erosion of transparency and accountability in journalism within the digital environment. In 2023, the lawsuit filed by The New York Times against OpenAI and Microsoft, concerning the use of journalistic content to train AI models without the consent of copyright holders, underscored the urgency of incorporating intellectual property rights and technology law into journalism curricula (The New York Times, 2023).

Contemporary media audiences have become increasingly accustomed to the presence of AI in everyday life. While the initial resistance to the sophistication of arti AI ficial intelligence has largely subsided, public skepticism persists regarding transparency, algorithmic bias, and the potential erosion of journalistic authority. Changes in news production processes, risks related to intellectual property, and audience attitudes collectively indicate that the role of journalists is increasingly shifting toward editorial verification and oversight, rather than manual execution of routine reporting tasks.

This shift places new demands on journalism education. Training programs must equip students with technical competencies in using AI tools, verifying AI-generated content, and managing editorial workflows within digital newsrooms. At the same time, it is essential to strengthen ethical and legal competencies, enabling students to identify emerging issues, mitigate algorithmic bias, and ensure the transparent and responsible use of AI, thereby safeguarding journalistic integrity.

1.2. Trends in integrating AI into professional journalism education

The development of artificial intelligence (AI) has not only transformed news production processes within media organizations but has also compelled journalism education institutions to restructure their curricula toward data-driven, algorithm - oriented, and computational thinking - based approaches. According to Carlson (2023, p. 458), contemporary journalism education can no longer focus solely on traditional competencies such as writing, editing, and professional ethics. Instead, it must equip students with the ability to interact with AI systems as an integral component of professional journalistic practice.

In the United States, journalism schools have increasingly incorporated AI and data journalism courses into their mandatory curricula. For example, the Columbia School of Journalism offers courses such as Computational Journalism and AI in the Newsroom, through which students are trained in large-scale data exploitation, content analysis algorithm development, and the use of language models to support news production (Pavlik, 2021, pp. 132-135). Similarly, the University of Missouri and Northwestern University have implemented comparable programs that emphasize interdisciplinary collaboration among journalism, computer science, and statistics as a core requirement for journalists in the digital environment. In Europe, institutions such as Cardiff University (United Kingdom) and the University of Amsterdam (the Netherlands) have integrated AI into journalism education through the development of “data journalists” and “computational journalists.” In these programs, students are required to undertake projects that simulate newsroom workflows using AI for information extraction, analysis, and visualization (Lewis & Westlund, 2022).

According to Pavlik (2021, p. 140), journalism education programs in the AI era should encompass four core competency domains: (1) foundational journalism competencies, including writing, editing, ethics, and media law; (2) data and algorithmic competencies, such as data analysis, information visualization, and an understanding of machine learning; (3) media technology competencies, including multimedia production, digital platforms, and automated content production; and (4) critical and ethical AI competencies, involving the assessment of algorithmic bias risks and the protection of privacy and copyright.

The 2024 Journalism AI report indicates that among 62 universities across 20 countries surveyed, as many as 71% have AI intelligence into journalism education curricula at varying levels. Specifically, 38% have developed standalone courses on AI in journalism, 33% have embedded AI into courses such as data journalism and multimedia journalism, while 29% have yet to introduce any formal AI-related content. However, the authors note that the degree of AI integration remains uneven and is significantly dependent on faculty expertise and the technological infrastructure available at each institution (JournalismAI, 2024, pp. 18-19). In response to the rapid pace of AI development, many journalism programs have shortened curriculum revision cycles from three to four years to approximately 12–18 months. Additionally, course structures have been updated to reflect emerging contexts, with subjects such as Data Journalism, Automated Journalism, and AI Ethics in Media increasingly becoming mandatory rather than elective components.

Beyond curricular changes, pedagogical methods in journalism education are also undergoing profound transformation. Moving away from traditional lecture-based and skills-simulation models, institutions are increasingly adopting project-based learning and experiential education within digital environments. Students engage with AI not merely as tool users but as individuals capable of understanding and critically evaluating these systems. Accordingly, journalism students are required to practice using AI for data collection, compare AI-generated outputs with human-produced journalistic works, and assess the reliability, bias, and ethical implications of AI-assisted content. The application of AI-supported project-based teaching models has been shown to enhance students’ cross-platform skill development by approximately 20-40% compared to traditional teaching approaches.

Nevertheless, excessive reliance on AI in learning also raises concerns about the potential erosion of independent thinking if autonomy and oversight are lacking. Consequently, many journalism education institutions that have adopted AI have established regulations governing AI use in learning activities to ensure that AI serves a supportive role rather than replacing students’ cognitive processes and journalistic skills (UNESCO, 2023, p. 17).

One of the most significant barriers to integrating AI into journalism education lies in faculty capacity. Many journalism educators were trained under traditional models that emphasize theory and journalistic practice but provide limited exposure to data science, computing, and algorithms. In addition, deficiencies in technological infrastructure - such as specialized computing systems for data analysis, licensed software, large-scale databases, and AI platforms tailored for education - pose substantial challenges to the systematic implementation of AI-related programs. Therefore, alongside continuous technological updates, collaboration with technology enterprises and modern newsrooms, and the promotion of interdisciplinary training that combines journalism with information technology, journalism education institutions increasingly regard the development of AI competencies among faculty as a strategic priority.

The ethical and legal challenges posed by AI have also compelled leading journalism education institutions worldwide to integrate these dimensions into their curricula. In the AI era, students must develop new forms of professional ethical competence and a clear understanding of the legal boundaries governing AI use. They must recognize journalists’ responsibilities in the exploitation of AI-assisted content and acknowledge the central role of human oversight and ultimate accountability for journalistic products.

In Viet Nam, as the impact of AI on journalism education has become increasingly evident, the period from 2023 to 2025 has witnessed a growing number of academic conferences, workshops, and forums dedicated to AI-related themes. Notably, the Academy of Journalism and Communication organized a series of scientific seminars and thematic conferences in 2023 - 2024 focusing on digital transformation, generative AI, and the future of journalism, while academic activities related to data journalism and digital communication have been actively conducted at the University of Social Sciences and Humanities, Vietnam National University, Hanoi.

At the curriculum level, several journalism education institutions in Viet Nam have piloted courses related to data journalism, digital communication, and digital skills. However, AI-related content is typically embedded within existing courses or offered as electives, rather than being defined as a mandatory professional competency for journalism students. This situation aligns with the Ministry of Education and Training’s broader assessment that the integration of advanced digital competencies, including AI, into higher education in Viet Nam remains fragmented and lacks a unified national framework (Ministry of Education and Training, 2023).

2. Impacts of AI on the Learning Capacity of Journalism Students

2.1. Information on Survey Participants

To assess the impact of AI on the learning capacity of journalism students, the author conducted a survey of 160 journalism majors from the following institutions: the Academy of Journalism and Communication; the University of Social Sciences and Humanities – Vietnam National University, Hanoi; Hue University; and the University of Da Nang. Each institution distributed 40 questionnaires.

Total number of questionnaires distributed: 160

Total number of valid responses: 135/160

2.2. Survey Results

 Journalism students’ awareness of the role of AI in professional practice

The survey results from 135 valid responses indicate that the majority of journalism students have developed a relatively clear awareness of the increasingly important role of AI in journalistic practice. Specifically, 82.2% of respondents believe that AI will become an indispensable support tool in journalistic operations within the next 5-10 years; 11.1% consider AI to play only a supplementary role in certain technical stages; while only 6.7% believe that AI will have little impact on the journalism profession.

These findings are consistent with Pavlik’s observation (2021, p. 128), which suggests that younger generations of journalists tend to accept AI as an inevitable component of the digital media environment, in contrast to the more cautious attitudes often found among traditional journalists.

However, when examining the depth of understanding, only 37.8% of students reported that they “understand fairly well” how AI systems operate in journalism, whereas 49.6% indicated that they “have only a general understanding,” and 12.6% admitted that they “almost do not understand” AI at all. This reveals a noticeable gap between students’ awareness of AI’s importance and their actual level of professional knowledge.

Degree of AI integration in current journalism training programs

The survey results show that the integration of AI into journalism training programs in Vietnam remains at an early stage. Specifically, 64.4% of students reported that their curricula do not include a dedicated course on AI in journalism; 25.2% stated that AI content is only embedded in certain courses such as Data Journalism or Multimedia Communication; and only 10.4% felt that they had received relatively systematic instruction on AI in relation to journalism.

This situation reflects a certain delay compared to international trends. While AI has become an official component of journalism education in many developed countries, in numerous developing contexts AI is still largely treated as a supplementary topic rather than a core element of training.

According to students, the main causes of this issue lie in limited teaching and learning resources related to AI, including a shortage of specialized instructors, a lack of Vietnamese-language textbooks and reference materials, as well as constraints in technological infrastructure and equipment.

AI-related skills equipped for students

Regarding specific skills, the survey findings indicate that 58.5% of students are able to use AI to support news writing, text summarization, and basic editing; 41.5% can apply AI in data searching and processing; 36.4% use AI to develop outlines or scripts for journalistic products; and only 18.5% possess the ability to analyze large datasets or design advanced AI-driven workflows.

These results suggest that AI is currently used primarily as a convenience tool rather than being leveraged as a strategic technological platform in journalism education. This observation aligns with Lewis and Westlund (2022, p. 10), who argue that contemporary journalism training tends to focus on “tool use” rather than “system understanding.”

3. Discussion and Recommendations

3.1. Discussion

The survey findings and practical observations indicate that journalism education in Vietnam is currently facing significant gaps and challenges in the context of the increasingly profound impact of AI on the journalism sector. First, there is still no national competency framework for AI in journalism education, resulting in inconsistencies in training objectives, curricular content, and teaching methods across institutions. Second, the linkage between universities and domestic newsrooms remains relatively weak, which limits the effective implementation of practice-based learning and newsroom simulation activities that integrate AI into journalistic workflows. Third, Vietnam lacks specific institutional policies governing the use of AI in academic environments, including regulations on ethics, transparency, and accountability for journalism educators and students.

AI is reshaping journalism education not merely at the level of tools, but at a structural level - transforming how instructors teach, how students learn, and how training institutions redefine professional learning outcomes. This section discusses three key issues and proposes reform-oriented recommendations that are appropriate to the context of journalism education in Vietnam.

(1) Shifting from “skills-based training” to “AI-integrated competency development”

The development of AI and its new demands on professional skills does not imply the elimination of traditional journalistic skills; rather, it expands their scope. News writing must now be accompanied by the ability to supervise and verify AI-generated content; interviewing requires the capacity to detect synthetic content and avoid manipulation; and editing increasingly involves assessing the training data sources of AI systems.

Accordingly, journalism curricula need to move from a skills-based approach to a competency-based approach, encompassing algorithmic critical thinking, management of AI-integrated production workflows, and information resilience capabilities. With 72% of surveyed students reporting a tendency to rely heavily on AI, training programs must establish clear principles to mitigate overdependence and to correct misconceptions regarding the accuracy and reliability of AI systems.

(2) Repositioning the role of instructors from “content delivery” to “learning design”

AI is redefining the pedagogical role of journalism instructors. As information and tools become widely accessible, the core value of instructors lies in designing learning tasks, guiding responsible AI use, and creating experiential learning environments that simulate AI-integrated newsroom processes. However, survey results indicate that only 36% of students reported that instructors “use AI as part of their teaching,” suggesting that many educators still view AI as external to teaching and learning activities.

Evidence from international newsrooms that have successfully integrated AI into journalistic production shows that professional journalists do not necessarily require programming skills, but they do need the ability to organize and manage AI-supported journalistic workflows. Therefore, professional development and upskilling programs for instructors in applied AI, AI ethics, and digital pedagogical skills are critically necessary.

(3) Establishing new ethical and safety standards in journalism education curricula

The rapid increase of deepfakes, disinformation, and algorithmic manipulation necessitates the urgent integration of ethics education related to AI use into journalism curricula. Although 82.5% of surveyed students reported awareness of these risks, only 24.7% actively sought to understand them in a systematic manner.

A UNESCO report (2023, p. 15) recommends that journalism schools implement AI ethics modules covering algorithm attribution, verification of AI-generated data, bias assessment, and rules to prevent AI misuse in news production. In the Vietnamese context, these issues are particularly critical, as journalism serves not only an informational function but also an ideological and public opinion - shaping role within society.

3.2. Recommendations

Based on the analysis of international trends and domestic training practices, this study proposes four core reform directions aimed at restructuring journalism education in Vietnam in a context where AI is increasingly becoming an inseparable component of modern journalistic practice.

First, standardizing AI competencies within journalism curricula

Journalism education needs to shift from an approach that merely introduces or sporadically integrates technology toward the standardization of AI competencies as a core set of learning outcomes for journalism students. Accordingly, this study proposes the inclusion of a compulsory undergraduate course entitled “AI in Journalistic Practice”, rather than limiting AI-related content to embedded modules or elective courses.

The minimum AI competency framework for journalism students should comprise four key components: (i) AI-supported journalistic practice competencies, including the use of AI tools in content gathering, processing, and production; (ii) AI verification and oversight competencies, encompassing the assessment of reliability, detection of bias, and identification of algorithmic risks; (iii) Ethical and legal competencies related to AI; and (iv) Competencies for participation in AI-integrated news production workflows within digital newsroom environments.

Such standardization would help ensure consistency in learning outcomes across training institutions and provide a foundation for quality assurance and program accreditation.

Second, redesigning courses based on an “AI-integrated simulated newsroom” model

Curricular innovation must be accompanied by reforms in teaching methods and learning environments, notably through the adoption of an “AI-integrated simulated newsroom” model. Under this approach, each professional course should be designed around at least one project that simulates the operational processes of a modern newsroom, incorporating AI tools at specific stages of journalistic production.

Learning projects should move beyond AI-assisted news writing to include simulations of data collection and analysis, the use of content analysis systems, audience feedback assessment, and quality control of AI-supported outputs. This approach enables students to develop systems thinking in digital journalism while enhancing teamwork skills, editorial decision-making, and professional accountability in technology-mediated environments.

Third, developing faculty capacity through a “pedagogy + AI” model

One of the major barriers to integrating AI into journalism education lies in faculty members’ technological capacity and pedagogical preparedness. This study therefore proposes a faculty development model that combines journalism pedagogy with foundational AI literacy, rather than expecting instructors to become advanced technology specialists.

Specifically, training institutions should organize professional development programs on applied AI for instructors, focusing on instructional design, learning assessment, and the guided and responsible use of AI by students. In parallel, establishing inter-institutional professional communities that bring together journalism educators, data scientists, and information technology specialists would facilitate resource sharing, experience exchange, and a reduction in fragmentation in curriculum implementation.

Fourth, establishing a code of AI ethics for journalism education

In addition to curricular and methodological reforms, AI integration requires a clear ethical and institutional framework within educational settings. This study proposes the development of a dedicated code of AI ethics for journalism education, including regulations on the scope of AI use in learning activities, principles of transparency and attribution of AI assistance, and boundaries designed to prevent technological misuse.

This code should draw upon international guidelines, such as UNESCO’s recommendations on AI in education and JournalismAI’s best practice principles, while being adapted to Vietnam’s legal context and journalistic culture. Institutionalizing ethical principles not only safeguards academic integrity but also helps students develop professional awareness and social responsibility from the training stage.

Conclusion

The study demonstrates that the rapid development of AI is reshaping journalistic competencies and necessitating deep pedagogical reforms within journalism education institutions. However, both students and instructors currently lack sufficient foundational AI competencies, while curricula and teaching methods have yet to keep pace with emerging professional standards. Accordingly, the study’s recommendations emphasize the standardization of AI competencies within journalism curricula, the adoption of AI-integrated simulated newsroom models, strengthened faculty development in digital pedagogy and AI ethics, and the establishment of institutional codes governing AI use in education.

This approach does not view AI as a threat to journalism, but rather as an opportunity to reinforce critical thinking, verification skills, and professional integrity. In a global media environment increasingly shaped by augmented digital ecosystems, reconfiguring journalism education toward AI integration is a necessary condition for ensuring the quality of human resources and sustaining journalism’s role as a cornerstone of mass communication./.

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