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Review

A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals

by
Tugce Ertem-Eray
* and
Yang Cheng
*
Department of Communication, North Carolina State University, Raleigh, NC 27695, USA
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2025, 15(3), 1058; https://doi.org/10.3390/app15031058
Submission received: 18 November 2024 / Revised: 9 January 2025 / Accepted: 16 January 2025 / Published: 22 January 2025
(This article belongs to the Special Issue From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress)
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Abstract

:
This study analyzes artificial intelligence (AI) research in communication scholarship through a content analysis of published articles between 2006 and 2022. It aims to understand the status of AI research between 2006 and 2022 and identify directions for future inquiry. Findings indicate that the number of articles about AI has increased over the years and scholars should continue applying existing theoretical frameworks or proposing new ones to investigate diverse topics across cultural and sociopolitical contexts.

1. Introduction

Artificial intelligence (AI) was born as a field of research in 1956 with the famous conference at Dartmouth College, when a small group of scientists gathered for a research project on AI. From 1957 to 1974, AI grew as a field of research with computers that could store more information and be faster, cheaper, and more accessible. Machine learning algorithms have advanced, and people have grown in understanding of applying algorithms to their problems [1]. During the 1990s and 2000s, developers achieved many landmark AI goals, such as Windows’ speech recognition software and IBM’s chess-playing computer program. The rise of the internet and e-commerce brought new opportunities for collecting and analyzing data, and marketers used web analytics, search engine optimization, and email marketing to reach and engage their customers. During the 2010s and 2020s, developers unveiled a new wave of AI applications, such as big data, cloud computing, natural language processing, computer vision, and deep learning. For example, marketers began using AI to create content, design campaigns, and predict outcomes to boost their customer experience [2]. We now live in an era in which the application of AI has been quite valuable in several domains, and communication is one of them.
Communication scholars have also started to focus on AI in their research. For example, some studies [3,4,5,6] cast a spotlight on the transformative impact of AI within the realm of journalism, offering nuanced perspectives on AI’s role, challenges, and implications for the profession. The exploration of AI within the advertising domain unfolds as a dynamic and transformative journey. Campbell et al. [7,8] delved into the impact of creative AI technologies, such as deepfakes and generative adversarial networks, on the production and editing of audiovisual content in advertising. In the realm of mass communications, for example, Brewer et al. [9] conducted a comprehensive exploration of public perceptions of AI through the lenses of news media engagement, science fiction exposure, and technology discussions. Hermann [10] took a closer look at the substantial impact of AI on communication, specifically focusing on mass personalization.
While the discussion surrounding AI within the communication field has seen a surge in articles, there remains a notable absence of a systematic review of the literature. Such an analysis is crucial because it allows for scholars to discern the prevalent research topics, theoretical frameworks, and methodological approaches employed within this domain, thereby guiding future research endeavors [11]. Consequently, this study endeavors to bridge this gap by conducting a comprehensive examination of published peer-reviewed articles focusing on AI within the communication field. Thus, this article serves several critical purposes within the academic landscape. First, it addresses the pressing need for a systematic review of published peer-reviewed articles on AI within the communication field, a topic experiencing burgeoning interest but lacking comprehensive analysis. By undertaking this review, this article aims to provide scholars with a comprehensive understanding of the trends, topical themes, theoretical frameworks, and methodological approaches shaping AI research in communication. Second, it seeks to offer insights into the diverse applications of AI in communication, ranging from chatbots and deepfakes to social bots and algorithms, thereby highlighting the multifaceted nature of AI’s impact. Additionally, this article endeavors to establish a robust and nuanced definition of AI within the context of communication studies, fostering clarity and coherence in future research endeavors. Finally, by identifying gaps and areas for further exploration, this study aims to guide and inspire future research efforts in this dynamic and rapidly evolving field. Overall, this article’s significance lies in its potential to inform and shape the trajectory of AI research in communication, facilitating advancements in theory, practice, and policy.

2. Literature Review

2.1. The Evolution of Artificial Intelligence as a Research Area in the Communication Field

AI is marked by seminal milestones that have shaped its progression over centuries. The concept of AI finds its roots in ancient tales and folklore, often portraying narratives of the creation of artificial beings [12]. However, the contemporary era of AI witnessed a notable surge in the mid-20th century, epitomized by the 1956 Dartmouth workshop—a watershed moment acknowledged as the inception of AI as a scholastic discipline [13]. The introduction of AI has led to an important turning point across a range of scholarly disciplines including the communication field.
The previous literature provided a panoramic view of the intersection between AI and the communication field, traversing various dimensions and implications [14,15,16,17,18]. Studies on the intersection between AI and the communication field begin with an exploration of attention and information consumption in the context of algorithms and decision support systems, revealing concerns about individual autonomy and ethical considerations. The European perspective emphasized the need for transparency and a human-centric approach to AI development [14].
Research also has examined human–machine communication (HMC), emphasizing the evolution of AI in roles historically exclusive to humans [19]. In addition, research in the communication field advocates for an expanded understanding of the implications of increasingly lifelike AI technologies on society. AI-mediated communication (AI-MC) emerges as a disruptive force within computer-mediated communication (CMC) [15]. The profound impact of AI on message content and the blurring of sender–receiver agency lines challenge traditional communication paradigms. The authors navigated dimensions like language impact, interpersonal dynamics, self-presentation, and trust, underscoring AI-MC’s ethical, cultural, and policy implications.
The academic inquiry extends its focus to communication, journalism, and advertising, examining the rapid and transformative evolution of AI’s significance in these industries [20,21]. For example, Cheng and Jiang [20] underscore AI’s remarkable ability to navigate extensive datasets, identify intricate patterns, and generate precise predictions, solidifying its indispensable role within these sectors. The initial phases of AI application in these fields primarily involved rudimentary data analysis and automated reporting. However, the pivotal juncture in AI’s impact coincided with the ascendancy of the internet and the advent of social media, propelling transformative shifts in communication practices [22].

2.1.1. AI and Journalism

AI’s influence on journalism has proven to be deeply impactful. In the initial stages, automated journalism experiments primarily concentrated on rudimentary data-driven reporting. However, with the progression of AI technologies, their capabilities have expanded exponentially [23]. Presently, AI algorithms possess the ability to analyze extensive datasets, discern emerging trends, and promptly generate insightful articles and reports. This not only streamlines the reporting process but also ensures the rapid and accurate dissemination of news to the public. Moreover, the potential of AI-driven content generation lies in its capacity to cater to individual reader preferences, providing a personalized news experience [24].
AI’s ascendancy in communication research has gained momentum with the advent of natural language processing and machine learning. These technological breakthroughs have paved the way for the implementation of chatbots, sentiment analysis tools, and automated content generation [25]. In the domain of journalism, AI-driven algorithms have become pivotal in the large-scale production of news articles and reports [26].

2.1.2. AI and Advertising

The advertising landscape has experienced a profound transformation through the integration of AI. Marketers now harness AI’s data analysis capabilities to gain insights into consumer behavior, preferences, and trends [27]. This empowers them to craft highly targeted and impactful advertising campaigns. AI algorithms can sift through consumer data derived from diverse sources, including social media, online browsing habits, and purchase history. This extensive analysis allows for advertisers to customize their messages, reaching specific demographics and even individual consumers. Consequently, advertising has evolved to become more cost-effective and efficient, ensuring marketing endeavors reach their intended audiences with enhanced precision [28]. In addition, AI has proven invaluable in optimizing ad targeting, refining pricing strategies, and fostering innovative content creation in the advertising industry.

2.1.3. Deepfake Technology

A prominent and widely debated phenomenon in recent years is the emergence of deepfake technology. This entails the creation of AI-generated videos or audio recordings that convincingly replicate real individuals, posing challenges in discerning authentic content from fabricated material. Deepfake technology raises substantial ethical and misinformation concerns, particularly in the realm of communication, given its potential to disseminate false information and damage reputations. Recent research endeavors underscore the imperative of developing AI-driven tools for detecting and mitigating deepfakes [29]. Researchers are actively involved in crafting algorithms capable of identifying manipulation indicators in videos and audio recordings. Despite their progress, they acknowledge being overwhelmed by the technical challenge of detection [30,31].

2.1.4. Communicative Robots

Moreover, communicative robots take the spotlight in media and communication studies, emphasizing their role as autonomous systems at the intersection of automated communication and communicative automation [16]. For example, Hepp [16] advocates for interdisciplinary research to comprehend these robots’ societal impact, providing a road map for understanding their influence on media production, social constructs, and power dynamics. In addition, the collection culminates with an exploration of AI’s nuanced impacts on language and social relationships [17]. Hohenstein et al. [17], highlighted the alteration of communication dynamics and the potential long-term effects on personal communication styles. It introduced trust, attribution, and the “moral crumple zone” concept, underscoring the intricate influence of AI-mediated communication on human interactions.
Against the backdrop of recent AI advancements, ethical considerations take center stage when individuals contemplate how they handle their data. For instance, a recent study on the ethical implications of AI in marketing [32] developed a conceptual model anchored in acceptance theory, risk perception, trust, and attitudes toward AI. The study emphasizes the significance of ethical and moral inquiries surrounding the adoption of AI in marketing. In summary, AI has undergone a substantial evolution, leaving an indelible mark on diverse sectors, including journalism and advertising.
Together, the current literature forms a comprehensive tapestry that underscores the multifaceted nature of AI’s impact on the communication discipline. From attention control and content creation to HMC, AI-MC, and communicative robots, the discourse delved into ethical considerations, theoretical frameworks, and the evolving dynamics of human–AI interaction. This body of work invited scholars and practitioners to navigate the complexities of AI in communication, urging a holistic understanding of its transformative influence on the discipline. Review studies aimed to identify the state of research about a specific topic during a specific period to analyze the development of that specific topic and understand future directions [33]. Existing review articles offered valuable insights into AI’s applications in communication networks, information systems, human communication, and strategic communication. However, critical gaps persist, notably those in the exploration of AI within specialized fields such as journalism, advertising, and mass communication. A comprehensive definition and delineation of AI’s scope in communication is lacking, requiring future research to clarify its features, technologies, and applications. To fill the gap, this study examines the trends of published articles focused on AI research in the communication field. Therefore, this study proposes the following research questions:
RQ1.
What are the trends of AI research in the communication field in published articles in peer-reviewed journals included in this study?
AI is an umbrella term that encompasses a variety of technologies, each defined by its capabilities, level of autonomy, and functionality, all designed to replicate human intelligence. Current applications such as voice assistants, chatbots, and recommendation engines are among the most widely used forms of AI, each designed to perform specific tasks or functions. As AI continues to advance, its capabilities have become increasingly sophisticated. Today, a range of AI technologies are utilized across industries, from processing human language to enhancing cybersecurity [34]. However, there remains a significant gap in the literature regarding which types of AI technologies are primarily studied within the field of communication. Thus, this study asks the following:
RQ1a.
What are the types of AI technologies mainly studied?
RQ1b.
What are the features of AI mentioned?
RQ2.
What is a comprehensive definition of AI in the communication field?
The current reviews provide overviews but lack in-depth analyses of the research methods and theoretical frameworks guiding AI studies in communication. In addition, analyzing research topics provides information about common and underrepresented topics that require further investigation; therefore, it could also provide future research ideas. Moreover, knowing authorial status might help students and scholars in terms of employment, school selection, and networking and as a starting point for further literature review. It could also provide an understanding of the degree of variety or uniformity among contributors to AI research in the communication field [33]. Thus, the third research question asks the following:
RQ3.
What are the authorial, topical, theoretical, and methodological status of AI research in the communication field in published articles in peer-reviewed journals included in this study?
Suggestions from future studies can help understand the directions AI research in the communication field should take [35]. Therefore, the fourth research question asks the following:
RQ4.
What are some suggestions to improve AI research in the communication field?

3. Method

We used the content analysis method to analyze published articles about AI in the communication field because analyzing published articles’ content provides knowledge about the development of academic research in a given field [36]. We used Web of Science, the world’s oldest and most widely used database that indexes leading research publications and citations [37], to find peer-reviewed journal articles in the communication field. Web of Science is one the most recognized multidisciplinary proprietary databases for peer-reviewed journal content. The Web of Science follows a comprehensive journal selection process that takes into account publication standards, expert evaluations, consistent publication, and the quality of citation data [38]. The unit of analysis for this study was peer-reviewed journal articles in the communication field. This study specifically focused on articles published within the broader communication discipline, encompassing areas like mass communication, public relations, advertising, and journalism. Therefore, we used “mass communication”, “new media”, “social media”, “public relations”, “strategic communication”, “advertising”, “journalism”, “marketing communications”, “Facebook”, “Instagram”, “Twitter”, “TikTok”, and “YouTube” as keywords to search relevant articles along with “artificial intelligence”. These social media platforms are the most popular social networks worldwide [39] that can benefit from the use of AI. Unlike automated systems, manual review enables subjective judgments and decisions based on the individual assessment of each article. In the systematic review search and selection process, a manual search of identified sources to select candidate papers based on abstract and title is a common way to exclude papers if they are clearly irrelevant [40]. As a result, the authors carefully examined each article to decide whether it should be included or excluded from the data analysis. The authors set up clear and standardized criteria for inclusion and exclusion (for instance, the written language of the articles must be English, and it must be empirical articles instead of review articles). Moreover, multiple reviewers (the two coders and the two authors) performed each step of the selection, data collection, and analysis to ensure greater objectivity and consistency in the process. Thus, we manually reviewed all of the articles published in peer-reviewed journals and removed articles (N = 35) unrelated to AI. Thus, this study consists of all the articles (N = 137) that addressed AI in the communication field published in peer-reviewed journals between 2006 and 2022. The year 2006 is the start date because the first article related to AI was published in the communication field in 2006 in New Media & Society [41].

Measures

We used previous review studies [35,42,43] to create coding categories. Based on these studies, we coded articles into the following categories:
General information. We coded general information about an article, including title, journal publication, publication year, and authorship, in this category. We used this information to analyze the trends of AI research in the communication field in published articles in peer-reviewed journals included in this study. We also coded what types of AI technologies were mainly studied and what features of AI were mainly mentioned in this category.
Definitions of AI. If an article contained a definition of AI, we coded the definition to demonstrate the constructive definition of AI [35].
Research Topics. This category included the main subjects examined in each article in peer-reviewed journals included in this study. We inductively created each topic included during the analysis, including relationships between AI and public relations; AI in advertising; AI in journalism; challenges of using AI in communication practices; AI in different parts of the world; consumers’ experiences, attitudes, and perceptions of AI; and perceptions of communication practitioners.
Theoretical Framework/Concept. If an article mentioned a specific theory or concept, we coded the theory name [42].
Research Methods. We coded each article depending on the methodological approach they used, including quantitative methods (e.g., surveys, content analysis), qualitative methods (e.g., in-depth interviews, focus groups), and mixed methods.
Future Suggestions. We also coded articles according to future research suggestions that could offer a future direction to AI research in the communication field [35].
Intercoder Reliability. For conducting the assessment of intercoder reliability, two well-trained graduate students independently coded a significant portion of the articles, comprising approximately 26% of the total dataset (N = 37). Their rigorous training equipped them with the necessary skills and expertise to accurately interpret and apply the coding criteria. Coders’ collaborative efforts yielded a composite reliability level of 0.87, indicative of an acceptable agreement in their coding interpretations [44]. This demonstration of consistency and accuracy underscores the reliability and robustness of the coding process, bolstering the credibility of this study’s results.

4. Results

In response to RQ1, this study found a total of 137 peer-reviewed journal articles that addressed AI in the communication field. To summarize, 15 were published in Digital Journalism, 15 in New Media & Society, 13 in the Journal of Advertising, 7 in Social Media + Society, 5 in Convergence: The International Journal of Research Into New Media Technologies, 5 in Media Culture & Society, 5 in Profesional de la Información, 5 in Public Relations Review, 4 in the International Journal of Advertising, 4 in Journalism Practice, 3 in Brazilian Journalism Research, 3 in Communication & Society-Spain, 3 in Communication Studies, 3 in the International Journal of Communication, 3 in Journalism, 3 in Media and Communication, and 3 in the International Journal of Business Communication. Further, journals that published two articles addressing AI in the communication field included the Journal of Advertising Research, Journal of Computer-Mediated Communication, Journalism & Mass Communication Quarterly, Information Communication & Society, and Science Communication. Journals that published one article addressing AI in the communication field are listed in Table 1. These journals are categorized under the Communication Emerging Sources Citation and Social Sciences Citation Index generated by WoS.
As indicated in Figure 1, the number of articles addressing AI has steadily increased in the communication field between 2006 and 2022. In total, 47 (32.2%) articles were published in 2022, 39 (26.7%) in 2021, 24 (16.4%) in 2020, 18 (12.3%) in 2019, 3 (2.1%) in both 2018 and 2016, 2 (1.4%) in 2015, and 1 (0.7%) in 2006.
In response to RQ1a, which asks what type of AI technologies were mainly used, this study found the most examined AI technologies among these peer-reviewed journal articles to be informational chatbots [20,45], deepfakes [46,47], social bots [16,48], and algorithms [49,50].
This study, in reviewing the existing research, also examined the various features of AI mentioned in peer-reviewed journal articles. It identified several key functions: simulating human intelligence, enhancing human communication, facilitating information management, and supporting creative content processes. For instance, AI technologies encompass computer programs or systems that emulate human intelligence, including home-based voice control devices [51] and computer-generated individuals with human traits [52]. These autonomous systems serve the needs of human communication. They can respond to users’ demands synchronically, engage in human interaction, and even learn users’ behavior preferences [53].
These technologies can also collect, analyze, interpret, and disseminate information to support organizational decision-making and strategies in the areas of advertising, public relations, and journalism. For example, they can provide customized news feeds on commonly used social media platforms like Facebook or Twitter as well as news aggregators like Google News and Apple News, improve newsroom efficiency, and enhance the accuracy of news reporting [49]. These technologies can support different stages and types of creative processes in advertising and content creation. For example, they can serve as intelligent personal assistants for brand recommendations based on users’ characteristics and preferences [54]. These technologies can also perform tasks including writing data-driven stories, organizing and updating media lists, aiding in crisis management, converting and transcribing audio into text, following and predicting media trends, and monitoring and managing social media [55].
In response to RQ2, this study found that only six articles provided a comprehensive definition of AI in the communication field. Guzman and Lewis [19] addressed the term AI as “polysemous, encompassing efforts to understand human intelligence by recreating a mind within a machine and to develop technologies that perform tasks associated with some level of human intelligence” (p. 72). Galloway and Swiatek [56] defined AI in the public relations context as “technologies showing humanoid cognitive abilities and performing humanoid functions in undertaking public relations activities, independently or together with public relations practitioners” (p. 735).
Wu et al. [57] referred to AI in advertising practice and used the definition of “the activity devoted to making machines intelligent” such that it “enables an entity to function appropriately and with foresight in its environment” [58] (p. 13). Brennen et al. [59] referred to AI as not a single technology but rather a loosely defined set of algorithms, techniques, and technologies that offer a powerful “mathematical method for prediction” (p. 23) from a journalistic perspective. Igartua et al. [60] defined AI as “computer systems that can make predictions, recommendations, or decisions and select information autonomously using algorithms and has been considered as one of the promising technological breakthroughs of the twenty-first century” (p. 2). Bingaman et al. [61] defined AI as “a computer program or system that mimics human thought—[and] has been portrayed in media messages as both a tool of social progress and a Pandora’s box filled with dangers” (p. 389).
RQ3 asked about the authorial, topical, theoretical, and methodological status of AI research in the communication field in published articles in peer-reviewed journals included in this study. Regarding authorship, a few scholars draw attention: Seth C Lewis has published five articles, whereas Andrea L. Guzman and Simone Natale have published four articles each in peer-reviewed journals included in this study. In addition, Colin Campbell, Jan Kietzmann, Kirk Plangger, and Sean Sands have published three articles each (see Table 2).
Regarding topical status, the peer-reviewed journal articles included in this study cover a variety of topics. There are articles specifically focused on the relationship between AI and public relations. For example, Galloway and Swiatek [56] examined the growing relationship between AI and public relations. Men et al. [45], analyzed the potential of chatbots as a strategic tool for public relations to see whether chatbots can improve key public relations outcomes. Panda et al. [55] discussed AI’s concepts, benefits, applications, impacts, and roles in the public relations industry. Some articles covered the use of AI in advertising and how AI has impacted the advertising process [8,57,62,63], whereas some articles focused on journalism practices [64,65,66].
Some topics focused on the challenges of using AI. For example, de Lima Santos and Salaverría [67] explained the hurdles and obstacles to using computer vision news projects, a subset of AI, in Latin American news organizations. Sjøvaag and Owren [68] discussed the challenges local newspapers face as the digital economy transitions to AI. Panda et al. [55] also talked about the new challenges and opportunities AI has brought to public relations functions. Topics have also covered the use of AI in different parts of the world. For example, Kothari and Cruikshank [69] focused on AI use in news production in Africa, whereas Xi and Latif [70] talked about the transformation of Chinese news media.
Túñez-López et al. [71] discussed how AI impacts the Spanish media ecosystem, whereas Schwartz and Mahnke [72] addressed how young Danish adults engage in a communicative relationship with Facebook’s news feed as algorithmic technology. Some articles [73,74,75] analyzed consumers’ experiences, attitudes, and perceptions of AI, whereas some articles covered the perceptions of communication practitioners. For example, Bastian et al. [76] covered how media practitioners in different departments (journalists, data scientists, and product managers) perceive the impact of algorithmic news recommenders. Yu and Huang [77] examined media practitioners’ perceptions of working in the Chinese media industry with the impact of AI on media employment.
Regarding the theoretical status, among 137 articles, 55 (40%) of them applied a theoretical framework to their studies. The most frequently applied theoretical framework was the expectancy violation theory (N = 3), which is an “interpersonal communication theory that makes the counterintuitive claim that violations of expectations are sometimes preferable to confirmations of expectations” [78], (p. 1). The articles included in this study also applied approaches such as agenda-setting, framing, gatekeeping theory, social identity theory, diffusion of innovations, elaboration-likelihood model, sociotechnical theoretical framework, and use and gratification theory to their studies. For instance, since the applications of artificial intelligence (AI) become increasingly widespread in society, how public perceives this technology has become important for its development, adoption, and sustainability. Thus, how news coverage, science fiction films, television programs, and trade journals offer storylines to understand AI have become a significant area scholars apply framing and agenda setting theories because these sources can have an effect on how members of the public think about AI [9,79]. Studies have also focused on how framing differs around AI among different actors. For example, Zeng et al. [80] analyzed how social media can challenge the official narratives around AI whereas Igartua et al. [60] examined the persuasive impact of testimonial narrative messages on AI. Nguyen and Hekman [81] conducted a comparative analysis of framing China and the U.S.A. in AI news reporting.
The multimethod approach was the dominant method used in the peer-reviewed articles covered in this study. Surveys were used most (18 articles), followed by case studies (17), interviews (16), conceptual/theoretical papers (14), experiments (11), content analysis and textual analysis (6), network analysis (5), and literature and theoretical review (4), as well as meta-analysis, rhetorical analysis, and systematic review (1 article). Furthermore, 16 articles did not mention any research methods in their studies (see Table 3).
The final research question concerned future suggestions to improve AI research in the communication field. Most of the articles recommended using a variety of methodological frameworks to improve AI research in the communication field, such as surveys and interviews [80], experiments [82], and mixed method design [83]. Some articles mentioned the importance of applying existing theoretical frameworks or proposing new ones to study AI in the communication field. For example, Sundar and Lee [18] emphasized the importance of critically evaluating the relevance and utilization of existing theories and research findings and proposing new ones as needed for communication scholars because of the rapid and fundamental changes in performing communication.
Lin and Lewis [84] stated that future theoretical explorations needed to incorporate a more realistic appraisal of journalism within different social, cultural, and political realities. Moreover, Shin [73] emphasized the importance of further examination of theories in AI research for theoretical justification. One of the common suggestions [9,61,85] was expanding the current studies into other measures to predict users’ perceptions and attitudes toward AI technologies. Some articles also suggested conducting comparative studies [76,86], whereas some [4,85] focused on the importance of replication of AI research in different sociopolitical contexts.

5. Discussion

The goal of this study is to provide a broad review of AI research in the communication field. Findings indicated that the number of articles about AI has increased over the years, which indicates that AI has had an impact on the communication field and that scholars have started to pay attention to this topic. This finding is not surprising because “communication is fundamental to both the theory and practice of AI” [87] (p. 2). Most articles were published after 2020, specifically in the journals Digital Journalism and New Media and Society. Digital Journalism is one journal advancing international research into digital journalism studies.
Journalism is one of the areas in the communication field that AI technology has affected enormously, and there has been a transformative influence of AI on journalism studies over the years, with a focus on AI’s role, challenges, and implications for the profession [4,64]. New Media and Society is another important outlet in the communication field that engages in discussions of the issues arising as a result of new information and communication technologies in the field. This outlet provides an interdisciplinary forum and publishes articles from a wide range of disciplines. These could be the reasons a high proportion of articles were published in these two outlets. Future researchers may therefore need to consider publishing their AI research in public relations journals (e.g., Journal of Public Relations Research, Public Relations Journal) and critical and cultural studies journals (e.g., Journal of Media & Cultural Studies, Critical Studies in Media Communication, Journal of Communication Inquiry).
Findings also indicated that chatbots, deepfakes, social bots, and algorithms were the most used AI technologies. Moreover, computer programs or systems that simulate human intelligence, such as home-based voice control devices, computer-generated individuals who have human traits and characteristics, and computer systems that perform tasks that would normally require human intelligence, were the AI features most mentioned in peer-reviewed journal articles. These features can be categorized into four dimensions: simulating human intelligence, enhancing human communication, facilitating information management, and supporting creative content processes. For example, the prevalence of chatbots and virtual assistants, fueled by AI, has become a standard feature in customer service and public relations strategies. These AI-driven interfaces provide instant responses to customer inquiries, elevating customer satisfaction and engagement levels [88] by supporting creative content processes.
The landscape of contemporary AI applications in communication has reached intricate levels. The ubiquitous presence of chatbots and virtual assistants, exemplified by Siri and Alexa, delivers instantaneous responses to user queries by simulating human intelligence. AI-driven recommendation systems, as exemplified by platforms like Netflix and YouTube, deploy sophisticated algorithms to enhance the user experience through content suggestions [89]. Within journalism, AI technologies are actively employed to curate news feeds and even generate articles, relieving human journalists of some workload and facilitating real-time event coverage.
The shift in advertising practices from traditional human-led methods to synthetic AI-facilitated advertising has become one of the central themes in the domain [90]. For example, in their chapter, Wu and Wen [91] offered an overview of the anticipated role of AI and machine learning throughout the stages of advertising. The integration of advancements in neuroscience, particularly in measuring attention, cognitive processing, emotional response, and memory, has facilitated the progress of AI and machine learning tools. These technologies can now identify crucial variables that drive more effective advertising and forecast improved performance.
Additionally, in terms of enhancing human communication, AI plays a pivotal role in analyzing social media conversations and trends, enabling organizations to monitor public sentiment and adjust communication strategies in real time. Such adaptability is paramount in an era where news and opinions can rapidly disseminate through digital channels. The realm of social media and mass communication investigations has also embraced AI techniques for a comprehensive analysis of the vast data emanating from online platforms. Users deploy AI algorithms to detect trends, conduct sentiment analysis, and even customize user content [88]. Researchers have delved into AI utilization for content recommendations and precise audience targeting in the advertising domain, further enhancing AI’s integration into investigative practices.
In the realm of mass communication, for example, AI also facilitates information management. For instance, Zhao [92] provided a direct contribution to understanding AI’s influence on mass communication by delving into the evolving landscape of intelligent media. The article offered a comprehensive overview of how AI, particularly in machine learning and deep learning, is reshaping human information production and dissemination activities. The insights provided in this article help discern the expanding boundaries of intelligent media development AI technology facilitates, indicating a significant shift in how communication occurs in the digital age [92]. Recent AI advancements have led to the development of powerful tools and applications, encompassing chatbots, recommendation systems, and deepfake technology. These advancements present a convergence of opportunities and challenges for the industry, necessitating vigilant adaptation by researchers, practitioners, and policymakers to navigate the evolving AI landscape within the communication field.
This study has attempted to find a comprehensive definition of AI in the communication field based on the articles analyzed. According to the findings, a comprehensive definition of AI needs to represent all subfields, such as journalism, public relations, and advertising in the communication field. Thus, this study defines AI as a computer program or system that performs human functions associated with some level of human intelligence through algorithms, techniques, and technologies that offer a powerful mathematical method for making predictions, recommendations, and decisions and selecting information.
The results of the analysis of published articles found that the scholars who published more AI research are professors in journalism and marketing. Thus, scholars from other areas in the communication field, such as public relations and mass communications, should also contribute to AI research to advance AI research in the communication field. Findings also indicated that there is a national diversity among scholars who published AI research, which indicates that the editors are open to international manuscripts in the communication field and have started to balance the dominance of U.S. scholars. The theoretical frameworks applied to the articles reviewed in this study are also the ones applied mostly to journalism studies, such as agenda setting, framing, and gatekeeping theory. There were, however, no dominant theoretical frameworks applied to AI research, which could indicate that AI research in the communication field is in the maturation process. Similar to the findings of the articles reviewed in this study [18,84], we also recommend future studies to introduce and test existing theories in related fields as well as develop new ones for AI research in the communication field.
This study also found a variety of topics covered in peer-reviewed journal articles, which also indicates the diversity of the topics in the communication field related to AI research, including areas of public relations, advertising, and journalism. Among a variety of topics covered in peer-reviewed journal articles, this study found scholars did not pay enough attention to ethical considerations in AI research. A recent study on the ethical implications of AI in marketing [32] developed a conceptual model anchored in acceptance theory, risk perception, trust, and attitudes toward AI. The empirical examination, involving 200 consumers of AI marketing services, revealed that perceived risk significantly influences attitudes toward AI, ethical concerns, and perceived trust. Additionally, the research indicated a notable correlation between perceived risk, ethical concerns, and social norms.
This study emphasizes the significance of ethical and moral inquiries surrounding the adoption of AI in marketing. Therefore, it suggests focusing on AI ethical concerns and implications as future research topics in the areas of public relations, advertising, journalism, and mass communication. For example, scholars should examine issues including bias in data and algorithms, privacy concerns, transparency in decision-making processes, discrimination, and power over AI systems to prevent negative consequences of AI development and usage. Scholars can investigate, for instance, to what extent AI systems enhance recruitment quality in areas of public relations, advertising, journalism, and mass communication and what can be done to mitigate discriminatory hiring practices because of algorithmic bias. Scholars can also analyze the role of communication practitioners in addressing the public’s privacy concerns, mitigating potential risks by designing safeguards to prevent AI from making decisions that could harm the public and society as a whole. Scholars should also continue to conduct AI research in different parts of the world to analyze the effects of environmental factors such as political systems, technological infrastructure, media systems, and economic systems on AI implications. Moreover, more research on AI-powered tools and their effects and implications in public relations, advertising, journalism, and mass communication would provide significant improvements in AI research in the communication field. For example, scholars should analyze how communication practitioners in different media industries can best realign their roles and relationships between the publics and technology in their work. Furthermore, following Guzman and Lewis’ [93] suggestion, this study also argues that scholars and practitioners need to embrace a big-picture approach in terms of the practical, strategic, and ethical challenges posed by AI, so they can present a more encompassing and accurate picture of media work and how it is consumed by publics in the era of AI.
In terms of methodological approaches, the multimethod approach was the dominant method used in the peer-reviewed articles covered in this study. Moreover, this study found a balance between quantitative and qualitative approaches in AI research in the communication field. Most of the review studies in the communication field [33,94] found quantitative methods to be the dominant methodological approaches and suggested the use of qualitative methods to reach a balance because both approaches are considered complementary [95]. The findings of this study indicate that the communication field has started to use qualitative methods as much as quantitative ones and suggest that scholars should continue to use both approaches to provide methodological diversity in AI research in the communication field. Overall, even though journalism and marketing were the dominant areas of AI research in the communication field, there was a diverse range of topics, theoretical frameworks, and methodological approaches covered in publications worldwide.

6. Conclusions

Analyzing peer-reviewed journal articles related to AI research in the communication field can help scholars understand the stage of AI research, improve its scholarship, and outline directions for future research [35]. Thus, this study explored the trends in published articles on AI research within the communication field. It addressed key questions, including the main types of AI technologies studied, a comprehensive definition of AI in communication, and the authorial, topical, theoretical, and methodological aspects of AI research in peer-reviewed journals included in this study. Additionally, this study offered recommendations for advancing AI research in the communication field.
The development of AI systems for the communication field has evolved from nascent technology to more sophisticated and advanced tools that have transformed communication strategies into more dynamic, responsive, and personalized ones. As the communication field adapts to this transition, understanding AI systems and their current challenges is significant for the field to leverage AI’s potential [96]. AI’s capability to analyze large datasets to understand the public’s preferences, behaviors, and expectations is crucial to captivate and engage with effective communication technologies across various platforms. These opportunities have also created challenges for the field that need to be considered including ethical issues related to ensuring unbiased systems, protecting personal data as well as maintaining the accuracy of AI-generated information amidst the large amounts of data.
This study was limited to articles published in peer-reviewed journals in the communication field found in the Web of Science database. Therefore, future research can extend the scope of research to trade press articles, books, book chapters, and conference proceedings. In addition, other peer-reviewed journals that are not in the Web of Science database address AI from various perspectives should be included. Furthermore, one of the limitations of this review study that it did not include articles from 2023 and 2024, which overlooks advancements in AI during these years, including the development of advanced generative AI models and the emergence of new ethical and social challenges related to AI applications. As AI technology rapidly evolves, future reviews should consider extending the scope including articles from 2023 and 2024. This will ensure that the review reflects the most up-to-date information on AI trends. Despite these limitations, this study makes valuable contributions to AI research in the communication field. This study suggests using various methodological approaches, applying existing theoretical frameworks, or proposing new ones to continue investigating diverse topics across cultural and sociopolitical contexts.

7. Future Directions

As AI becomes increasingly powerful, its applications continue to expand between 2022 and 2024. While 2022 marked the year that generative AI captured public attention, 2023 was the year it began to establish a foothold in the business world. As a result, 2024 was a crucial year for AI’s future, with researchers and businesses working to determine the most effective ways to integrate this technology into our daily lives [97]. In the communication field, the recent developments can be incorporated into natural language processing (NPL), social media and content creation, media and journalism, marketing and customer communication, crisis communication and public relations, and ethical considerations and governance. While AI is transforming communication through effective and personalized content, it has also raised ethical and regulatory concerns within the field. Natural language processing (NLP) is a branch of computer science and AI that utilizes machine learning to allow computers to interpret and interact with human language. Today, NLP is a part of daily life, powering search engines, chatbots for customer service, voice-activated GPS systems, and digital assistants like Alexa, Siri, and Cortana [98]. The rapid advancements in NLP have changed the way individuals and organizations interact with language data, influencing applications like automated customer service, language translation, and sentiment analysis. Thus, these developments offer unique opportunities to improve communication and enhance customer service and understanding across languages and cultures. However, they also present significant challenges, including ethical dilemmas, concerns about data privacy, and the ongoing need for improvements in algorithmic accuracy and fairness [99]. Therefore, it is important to carefully evaluate the impact of integrating NLP technologies into daily life and address the challenges they present in the future.
The year 2024 was also when AI made a great impact on content creation [100]. The most common use cases for AI-powered written content include emails and newsletters (47%), text-based social media posts (46%), video-based social media content (46%), and blogs and long-form articles (38%) [101]. AI tools including ChatGPT, Jasper, and Copy.ai are now creating content faster, more efficient, and more impactful [102]. AI tools quickly analyze vast amounts of data and give marketers deep insight into consumer behavior. As AI evolves, digital marketing anticipates far-reaching impacts such as advanced customer segmentation to real-time personalization, deeper connections between organizations and publics, better decision-making, and stronger public engagement [103]. These AI impacts on content creation also bring challenges in terms of misinformation and the ethical use of deepfakes. Lundberg and Mozelius [104] identified some challenges, including fake news, bullying, defamation, media manipulation, and democracy damage. These transformations also raised concerns around journalism. The rise of generative AI systems, particularly, affects the entire news cycle, from information gathering to news dissemination, which raises questions revolving around issues such as transparency, accountability, responsibility, bias, and diversity [105].
AI is also revolutionizing the public relations industry specifically with media monitoring and sentiment analysis. It holds the potential for transformation including enabling brands to anticipate emerging trends, addressing crises before they escalate, and delivering highly relevant content with precision [106]. For instance, AI-driven crisis management includes technologies that analyze vast amounts of data, predict potential crises, and create responses. This process involves NLP, sentiment analysis, machine learning models, and data mining, which can help organizations identify the issues before they escalate and also empower them to craft effective communication strategies [107]. The key to unlocking AI’s full potential lies in balancing its power with the essential qualities of human empathy and creativity. This process will define the future of public relations, where technology and human insight collaborate to create lasting influence [106].
Moreover, all communication fields, including journalism (e.g., [105]), advertising (e.g., [108,109]), and public relations (e.g., [110,111,112]), are challenged by the ethical implications of AI. This study also highlights the importance of ethical and moral considerations in the adoption of AI within the communication field. As AI continues to shape the practices across journalism, advertising, public relations, and marketing, it is important to develop comprehensive frameworks for ethical decision-making. This includes equipping communication professionals with the resources to assess and navigate the ethical implications of their AI technologies. Scholars can work on the creation of ethical decision-making frameworks that guide communication industries in terms of how they should use AI technologies in their practices. This could include exploring the need for ethical training for communication professionals. Moreover, AI’s impact on privacy, transparency, and accountability are still crucial areas that need extensive exploration. Research into how communication professionals can responsibly address these challenges in their areas and exploring ways to prevent manipulation of information is important to build trust between publics and the organizations. Thus, future research should consider developing ethical frameworks, examining privacy and bias concerns, ensuring transparency, and finding ways to prepare communication professionals how to navigate AI challenges in their practices.
Despite the insights gained from this study, several areas call for further investigation to deepen our understanding of AI in the communication field. We believe that studying AI in all its forms, uses, and implications within specific industries is still important. However, we also believe that further research is needed to explore the implications of AI across various industries. Guzman and Lewis [93], for instance, suggested that researchers should step outside of their intellectual silos to analyze how the challenges and opportunities of AI are commonly shared across the media industries. Media professionals across different fields adopt similar AI technologies, including machine learning and natural language processing, for often similar purposes such as content creation and engaging with publics. Thus, instead of focusing solely on specific areas such as public relations or advertising, scholars should adopt more cross-industry approaches to develop a more comprehensive understanding of AI’s impact on the communication field. Therefore, we echo Guzman and Lewis’s [93] suggestions on embracing a big-picture approach as a future research direction to provide a more comprehensive and accurate understanding of AI in the communication field.

Funding

This research received the Global International Seed Grant, North Carolina State University.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to acknowledge Junyu Zhou and Jipeng Chen, who served as student coders for this study. They provided essential assistance in data coding, and their efforts were integral to the research process.

Conflicts of Interest

The authors declare no conflict of interest.

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Applsci 15 01058 g001
Figure 1. The number of articles about AI in peer-reviewed journals in the communication field between 2006 and 2022.
Figure 1. The number of articles about AI in peer-reviewed journals in the communication field between 2006 and 2022.
Applsci 15 01058 g001
Table 1. The number of AI articles published in peer-reviewed communication journals 2006–2022.
Table 1. The number of AI articles published in peer-reviewed communication journals 2006–2022.
Journal NameNumber of the Articles
Digital Journalism15
New Media & Society15
Journal of Advertising13
Social Media + Society7
The International Journal of Research Into New Media Technologies5
Media Culture & Society5
Profesional De La Informacion5
Public Relations Review5
International Journal of Advertising4
Journalism Practice4
Brazilian Journalism Research3
Communication & Society-Spain3
Communication Studies3
International Journal of Business Communication3
Journalism3
Media and communication3
Information Communication & Society2
International Journal of Business Communication2
Journal of Advertising Research2
Journal of Computer-Mediated Communication2
Journalism and Mass Communication Quarterly2
Science Communication2
African Journalism Studies1
Analisi-Quaderns De Comunicacio I Cultura1
Business and Professional Communication Quarterly1
Canadian Journal of Communication1
Chinese Journal of Communication1
Journal of Media & Cultural Studies1
Critical Studies in Media Communication1
Journal of Psychosocial Research on Cyberspace1
Global Media and China1
Health Communication1
Human Communication Research1
Internet Policy Review1
Journal of Broadcasting & Electronic Media1
Journal of Children and Media1
Journal of Communication1
Journal of Communication Inquiry1
Journal of Creative Communications1
Journal of Public Relations Research1
Journalism Studies1
Jurnal the Messenger1
Mass Communication and Society1
Nordicom Review1
Palabra Clave1
Policy and Internet1
Public Relations Inquiry1
Revista Latina De Comunicacion Social1
Journal of Media and Communication Research1
Telecommunication Policy1
Television & New Media1
Table 2. Authorship in AI research in the peer review communication journals 2006–2022.
Table 2. Authorship in AI research in the peer review communication journals 2006–2022.
AuthorThe Number of Articles
Seth C Lewis5
Andrea L. Guzman4
Simone Natale4
Colin Campbell3
Jan Kietzmann3
Kirk Plangger3
Sean Sands3
Ashley Paintsil2
Chen Lou2
David C. Wilson2
dos Santos, Marcio Carneiro2
James Bingaman2
Meredith Broussard2
Paul R. Brewer2
S. Shyam Sundar2
Taylor Jing Wen2
Table 3. Research methods in AI research in peer-reviewed communication journals 2006–2022.
Table 3. Research methods in AI research in peer-reviewed communication journals 2006–2022.
MethodNumber of the ArticlesPercentage
Multimethods2316.5%
   Case study and interviews521.7%
   Case study and survey28.7%
   Interviews and qualitative content analysis417.4%
   Literature review and network analysis14.3%
   Participant observation and interviews28.7%
   Participant observation and literature review14.3%
   Qualitative and quantitative content analysis14.3%
   Systematic review and quantitative content analysis14.3%
   Experiment and survey14.3%
   Case study and experiment28.7%
   Focus group and interviews and survey14.3%
Interviews and content analysis14.3%
Interviews and literature/theoretical review14.3%
Survey1812.9%
Case study1712.2%
Interviews1611.5%
No method1611.5%
Conceptual/theoretical paper1410.1%
Experiment117.9%
Content analysis64.3%
Textual analysis64.3%
Network analysis53.6%
Literature/theoretical review42.9%
Meta-analysis10.7%
Rhetorical analysis10.7%
Systematic Review10.7%
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Ertem-Eray, T.; Cheng, Y. A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals. Appl. Sci. 2025, 15, 1058. https://doi.org/10.3390/app15031058

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Ertem-Eray T, Cheng Y. A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals. Applied Sciences. 2025; 15(3):1058. https://doi.org/10.3390/app15031058

Chicago/Turabian Style

Ertem-Eray, Tugce, and Yang Cheng. 2025. "A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals" Applied Sciences 15, no. 3: 1058. https://doi.org/10.3390/app15031058

APA Style

Ertem-Eray, T., & Cheng, Y. (2025). A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals. Applied Sciences, 15(3), 1058. https://doi.org/10.3390/app15031058

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