Jurnal ASPIKOM.
Vol.
10 No.
, pp.
P-ISSN: 2087-0442.
E-ISSN: 2548-8309
DOI: http://dx.
org/10.
24329/aspikom.
AI and communication research: Reassessing theory and methodology in the age of automation Prahastiwi Utari1.
Pramana Pramana2.
Eka Nada Shofa Alkhajar3 Department of Communication Science.
Universitas Sebelas Maret.
Indonesia Corresponding Author, prahastiwi@staff.
1,2,3
Abstract Article History:
Received 2025-08-27.
Revised 2025-09-30.
Accepted 2025-12-02.
Published 2025-12-04.
Keywords:
Artificial Intelligence.
Beyond Human Communication.
Convergence Theories.
Research Methodology.
Digital Communication The rapid development of artificial intelligence (AI) marks a fundamental shift in communication science, positioning AI not only as a research tool but also as an active entity in the communication process.
This paper argues for a paradigm shift from conventional human to human interaction toward beyond human communication, where AI functions as communicator, medium, and message producer.
Such transformation requires re-examining communication theories in interpersonal, mass, and technological contexts within a convergent framework.
At the theoretical level, communication must be repositioned to address new ontological and epistemological questions.
Methodologically.
AI offers opportunities through big data and automated analysis while raising ethical challenges such as algorithmic bias and the risk of reducing communication to technical processes.
This paper proposes a reflective critical approach that emphasizes adaptive, convergent, and contextual communication theories, ensuring the discipline remains relevant in the era of artificial intelligence.
Introduction The rapid advancement of artificial intelligence (AI) has reshaped how communication occurs across interpersonal, organizational, and mass communication contexts.
Initially regarded merely as a computational tool for information processing (Haenlein & Kaplan, 2.
AI has evolved into an active participant in communicative exchanges.
Recent studies demonstrate that AI now functions as communicator, medium, facilitator, and autonomous content generator (Baptista & Gradim, 2022.
Florea & Croitoru, 2025.
Mieczkowski et al.
, 2021.
Sansanin & PenjieviN, 2.
augment, or even generate content to achieve communicative and relational goals.
AI-MC is increasingly involved in human communication and has the potential to impact core aspects of human communication, such as language production, interpersonal perception and task performance.
Through a between-subjects experimental design we examine how these processes are influenced when integrating AI-generated language in the form of suggested text responses (GoogleAos smart replies.
Complementing these global developments.
Indonesian communication scholarship also indicates the growing presence of AI as an active communicative entity.
study by NurAoaeni et al.
demonstrates that AI influencers are capable of performing strategic communication functions through interactive, personalized, and data-driven engagement AI and communication research: Reassessing theory and methodology in the age of automation with audiences (NurAoaeni et al.
, 2.
This development challenges long-standing humancentered assumptions in communication science, where meaning production and message interpretation were traditionally attributed exclusively to humans (Littlejohn et al.
, 2017,
This shift necessitates a change in perspective regarding the nature of communication.
Conventionally, communication in academic studies has been predominantly conceptualized as human-to-human interaction, wherein two individual function as communicators, employing communication media to exchange messages and cultivate a deeper understanding of one another (Littlejohn et al.
, 2.
The study of human communication has been approached from two primary perspectives: interpersonal communication and mass communication (Griffin et al.
, 2019.
Littlejohn et al.
, 2017.
West & Turner, 2.
However, the advent of artificial intelligence has precipitated the dissolution of these boundaries, giving rise to the notion of Aubeyond human communicationAy (Littlejohn et al.
, 2.
This concept denotes a mode of communication that transcends the human subject as the exclusive arbiter and principal agent of communication.
has become an integral component of a sophisticated and autonomous communication system that generates and mediates meaning in highly dynamic contexts.
The concept of AuBeyond Human Communication,Ay as elucidated by Littlejohn et al.
(Littlejohn et al.
2017, 2.
, challenges the conventional boundaries of communication.
The act of human communication with non-human entities offers a unique opportunity to observe a variety of communication methods.
This observation enables an examination of the underlying reasons for communication and the diverse approaches employed.
Guzman and Lewis .
emphasize the pragmatic use of AI, which can perform certain tasks similar to those performed by humans.
This is referred to as communicative AI (Guzman & Lewis, 2020.
This phenomenon has serious implications for the theoretical framework and research methodology of communication studies.
Many conventional communication theories are based on the assumption that communication occurs in a linear or circular manner between humans (Griffin et al.
, 2019.
Littlejohn et al.
, 2017.
West & Turner, 2.
As AI takes on various roles in communication elements (Hancock et al.
, 2.
augmenting, or generating messages to accomplish communication goals.
The recent advent of AI-MC raises new questions about how technology may shape human communication and requires reevaluation - and potentially expansion - of many of Computer-Mediated CommunicationAos (CMC, a shift occurs that demands cross-theoretical integration, as well as more convergent and adaptive approaches.
Reardon & Rogers .
Cathcart and Gumpert .
Hawkins and Wiemann .
Walther .
, and Cappella .
have proposed diverse approaches to unify, bridge, and integrate communication theories in response to the development of digital technology (Cappella, 2017.
Cathcart & Gumpert, 1983.
Hawkins et al.
, 1988.
Reardon & Rogers, 1988.
Walther, 2.
Terms such as rethinking, merging, integrating, and bridging are increasingly used to mark this spirit of unification.
The integration of artificial intelligence (AI) into the communication environment has begun to fundamentally reshape the research ecosystem long before methodological techniques are discussed.
Scholars have observed that AI does not merely generate new types of data but actively structures how data is produced, filtered, prioritized, and rendered meaningful, thereby altering the epistemic conditions of research itself (Elish & Boyd, 2018.
Garcya-Orosa et al.
, 2.
However, despite the growing scholarly attention to AI-mediated communication, existing studies remain fragmented, often focusing only on isolated components such as chatbot interaction, algorithmic filtering, or AI-generated content (Guzman & Lewis, 2020b.
Hancock & Levy, 2.
These strands of research rarely offer an integrated theoretical and methodological reflection connecting AIAos communicative roles to broader epistemological and ontological questions in communication science Jurnal ASPIKOM.
Vol.
10 No.
P-ISSN: 2087-0442.
E-ISSN: 2548-8309
Prahastiwi Utari.
Pramana Pramana.
Eka Nada Shofa Alkhajar (Garcya-Orosa et al.
, 2.
This gap highlights the need for a more holistic approach that unifies theoretical shifts with methodological implications, particularly in communication scholarship that must adapt to rapidly evolving digital ecosystems.
This shift introduces complex challenges, including questions about algorithmic opacity, data provenance, machine bias, and the extent to which AI-generated outputs can be considered authentic representations of communication phenomena (Ess, 2023.
Hancock et al.
, 2.
augmenting, or generating messages to accomplish communication The recent advent of AI-MC raises new questions about how technology may shape human communication and requires re-evaluation - and potentially expansion - of many of Computer-Mediated CommunicationAos (CMC.
At the same time.
AI-driven environments reshape the position of the researcher, who is no longer an external observer but a participant within an algorithmically mediated system (Guerra, 2.
As a result, debates surrounding methodological choices in communication research must begin with an understanding of these epistemological, ethical, and practical disruptions, which precede and frame any opportunities emerging from AI-assisted data collection and analysis.
Similarly, in terms of methodology.
AI opens new opportunities in the collection and analysis of communication However, these opportunities emerge alongside a broader methodological shift caused by the integration of AI into the communication ecosystem, which has already introduced unresolved ethical, epistemological, and practical challenges (Ess, 2.
Given this landscape, the present paper offers a consolidated perspective that connects theoretical convergence, methodological disruption, and the changing epistemic foundations of communication research in the AI era.
Unlike prior works that discuss these issues separately (Guzman & Lewis, 2020b.
Hancock & Levy, 2.
, this study positions them within a single analytical framework to illustrate how AI fundamentally reshapes the nature of communication as both a concept and an object of study.
This integrated approach serves as the paperAos primary contribution, providing a conceptual grounding for developing adaptive and context-responsive communication theories.
In light of the aforementioned context, the present study endeavours to critically examine the manner in which artificial intelligence (AI) is transforming the landscape of communication science research, both in theoretical and methodological respects.
The present paper elucidates the following:
A paradigm shift is occurring from human-to-human communication to beyond human The following inquiry is posed for further consideration: What are the implications of theoretical convergence in understanding the complexity of AIAos role in communication? The following discussion will address the methodological opportunities and challenges in AI-based communication research.
Consequently, this paper not only provides critical reflection but also establishes the foundation for the development of more contextual and responsive theories and methodologies in communication research that align with the evolving times.
Method This paper was developed using a systematic literature review approach (Triandini et al.
, 2.
, which is a systematic and reflective review of various books, journal articles, research reports, and digital documents relevant to the main objectives of examining: .
developments in communication theory and artificial intelligence (AI) technology.
methodological aspects that open up new opportunities in communication data collection Jurnal ASPIKOM.
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P-ISSN: 2087-0442.
E-ISSN: 2548-8309
AI and communication research: Reassessing theory and methodology in the age of automation and analysis.
The literature search was conducted using major academic databases, including Scopus.
Web of Science.
DOAJ, and Google Scholar, to ensure comprehensive coverage of communication and AI-related publications.
Search queries used combinations of keywords such as Auartificial intelligence,Ay Aucommunication theory,Ay AuAI-mediated communication,Ay Aubeyond human communication,Ay and Aualgorithmic communication,Ay connected through Boolean operators (AND/OR).
The search focused on publications from 2015 to 2025 to capture contemporary developments in AI while still including foundational communication theories as supporting references.
Inclusion criteria consisted of peer-reviewed journal articles, scholarly books, and research reports that specifically address AI in communication contexts.
Exclusion criteria removed non-academic sources, articles unrelated to communication science, and publications without accessible full texts.
A total of approximately 40Ae50 core publications were selected after screening titles, abstracts, and full texts based on relevance to the study.
The selected literature was analyzed using thematic synthesis to identify conceptual patterns related to paradigm shifts, theoretical convergence, and methodological disruptions in AIbased communication research.
This approach was selected because the present study is at a stage in which it is developing a theoretical argument through intertextual analysis, rather than through the collection of empirical data.
The extant literature on the subject has been reviewed, including classical and contemporary communication theories, studies on AI as a communicative entity, and critical thoughts on the convergence of human and machine communication.
The study incorporates a shift in methodological research concepts that, ontologically, distinguishes human units of analysis quantitatively, qualitatively, or as a combination of It also focuses on the category of shifting research subjects to non-human entities and developing methodologies that facilitate interaction with such non-human entities.
The author employs a dual approach of conceptual analysis and reflective-critical thinking to address the epistemological and ontological shifts occurring within the domain of communication studies in the context of artificial intelligence.
Consequently, this approach aligns with the primary objective of the paper, which is to formulate adaptive, contextual, and convergent theoretical and methodological propositions to address the paradigm shift in communication in the digital age.
Results and Discussion To address the studyAos guiding questions, the findings are organized into three key themes: .
the paradigm shift toward beyond-human communication, .
the theoretical convergence needed to account for AIAos multifaceted communicative roles, and .
the methodological, ethical, and epistemological disruptions emerging in AI-based communication research.
Together, these themes synthesize the reviewed literature and clarify how AI reshapes communication as both a theoretical and methodological domain.
Key Finding 1: Paradigm Shift in Communication Theory As with theories in other disciplines, communication theory is predicated on fundamental assumptions concerning the actors of communication, the modalities of communication, and the contexts in which meaning is created and conveyed (Griffin et al.
These theories are typically classified into various domains, including communicators, messages, media, and effects (Littlejohn et al.
, 2.
, levels .
, interpersonal, group, organizational, media.
(Griffin et al.
, 2.
), or specific contexts .
, politics, health, (Miller, 2.
The role of humans as the sole actors in communication is a fundamental characteristic of the entire communication process.
Jurnal ASPIKOM.
Vol.
10 No.
P-ISSN: 2087-0442.
E-ISSN: 2548-8309
Prahastiwi Utari.
Pramana Pramana.
Eka Nada Shofa Alkhajar Artificial intelligence (AI) now performs multiple communicative functions traditionally associated with human actors.
AI is not merely a channel but an active entity that shapes meaning, influences relational dynamics, and constructs the very structure of interpersonal communication (Pramana et al.
, 2.
AI is no longer merely an auxiliary tool but a force that systematically influences the work structure and roles of public relations professionals (Febriani et al.
, 2.
AI can receive and respond to messages .
, distribute and mediate information through digital interfaces .
, and autonomously generate content through adaptive machine-learning processes .
essage produce.
This consolidation of communicative roles illustrates why AI disrupts the basic assumptions of human-centered communication models.
This disruption signifies a paradigmatic shift that requires communication theory to expand beyond human-centered assumptions.
Consequently, communication theory must evolve to accommodate this paradigm shift, wherein communication transcends the human realm, encompassing non-human entities such as artificial intelligence (AI), algorithmic systems, and digital avatars.
This fundamental change gives rise to a number of theoretical consequences, including:
The paradigm of communication undergoes a transition, shifting from being conceptualized as an interactive process to being regarded as interoperability.
The term AuinteroperabilityAy refers to the capacity of systems to interact with one another, a capability that is of increasing importance in the domain of communication.
Interoperability facilitates the efficient and effective connection, communication, and sharing of critical data among disparate information technology (IT) systems within designated domains (Lindemulder & Kosinski, 2.
The beyond human communicationAyapproach (Guzman & Lewis, 2020.
posits that communication is not merely the exchange of symbols agreed upon by humans.
Rather, it is a dynamic process involving: Intelligent systems have the capacity to learn and adapt messages and interactions based on system logic rather than human intent.
They facilitate communication processes between humans and machines, as well as between machines .
achine-to-machine communicatio.
This transformation of the fundamental concepts of AusenderAy and AureceiverAy introduces the role of non-human communicators that lack human consciousness but perform communication functions The application of artificial intelligence in the domains of communication, as a medium, and the production of meaning In classical communication theory, the roles of communicator, medium, and message are typically distinguished clearly.
Theoretical frameworks are known to categorize these roles separately .
ee Littlejohn.
Griffin, and WestAos theoretical framework.
However, the integration of these disparate elements into a unified entity by AI serves to underscore the significance of this paradigm shift.
Artificial intelligence (AI) has the capacity to receive information and provide feedback.
Additionally.
AI employs algorithms or digital interfaces to disseminate information .
through various Additionally.
AI has the capacity to generate novel meaning .
ontent generato.
through the application of adaptive machine learning processes.
Consequently, theories of mass communication, interpersonal communication, and technology are no longer able to stand alone.
A more convergent and eclectic theoretical approach is necessary to facilitate researchersAo comprehension of the intricacies inherent in contemporary digital communication relationships.
Jurnal ASPIKOM.
Vol.
10 No.
P-ISSN: 2087-0442.
E-ISSN: 2548-8309
AI and communication research: Reassessing theory and methodology in the age of automation Taken together, these developments indicate that AI transforms the basic ontology of communication by introducing non-human entities capable of performing communicative This shift directly addresses the first research question by demonstrating that the foundations of human-centered communication theory are no longer sufficient for explaining AI-mediated interaction.
Key Finding 2: Theoretical Convergence for Understanding AI Theoretical convergence is imperative to bridge the gap between fragmented scientific To illustrate this point, consider the following example: In order to comprehend human interaction with chatbots, it is necessary to employ a combination of interpersonal .
theory, performative theory, and technology mediation theory.
In order to analyze the role of artificial intelligence in shaping public opinion, researchers must integrate the spiral of silence theory, framing theory, and filter bubble algorithm theory.
In order to examine the meaning generated by AI .
, text from ChatGPT), it is necessary to employ semiotics, discourse analysis, and human-machine interaction design In essence, effective communication in the context of AI necessitates a transparadigmatic approach to thinking, which involves transcending established traditions and exploring novel avenues for conceptualizing communication.
The shifts in methodology in communication research in the AI era.
The theoretical paradigm shift in dealing with artificial intelligence (AI) cannot be separated from changes in methodological approaches.
Theory can be conceptualized as a conceptual framework through which reality is viewed, while methodology represents the approach selected to understand and explain reality.
In the domain of communication science, methodology is not merely a technique or procedure.
it is also a reflection of how we think and perceive the world.
The integration of AI into communication systems indicates that contemporary communication reality is not solely constructed through natural processes but also through algorithmic means (Garcya-Orosa et al.
, 2.
This phenomenon gives rise to novel forms of data, new actors, and interactions that are no longer entirely human in Consequently, the methodology employed in the domain of communication research must undergo a transformation that encompasses both technical and philosophical dimensions.
Shifting the Dichotomy: The utilization of quantitative and qualitative methodologies alone is insufficient for a comprehensive investigation.
The traditional dichotomy between quantitative and qualitative approaches has become inadequate in capturing the complexity of digital communication.
AI operates by generating big data (Elish & Boyd, 2.
and massive data .
rom social media, virtual conversation recordings, user interactions with system.
, as well as meaning and symbols.
These need to be analyzed using interpretive approaches.
In light of these challenges, there is an imperative for novel approaches that can effectively address the complexity of these problems.
Hybrid or convergent methodologies, which integrate diverse fields of study or methods, are particularly well-suited for such endeavors.
These approaches may include, but are not limited to: Computational ethnography .
an Voorst & Ahlin, 2.
particularly ethnography.
As anthropologists of data and AI, we appreciate the growing recognition of qualitative methods.
However, we emphasize the importance of grounding ethnography in specific ways of engaging with oneAos field site for this method to be valuable.
Without this grounding, research outcomes on AI may become distorted.
In this commentary, we highlight three key Jurnal ASPIKOM.
Vol.
10 No.
P-ISSN: 2087-0442.
E-ISSN: 2548-8309
Prahastiwi Utari.
Pramana Pramana.
Eka Nada Shofa Alkhajar aspects of the ethnographic method that require special attention to conduct robust ethnographic studies of AI: committed fieldwork .
ven if the fieldwork period is short is a methodological framework that integrates participatory observation with digital data analysis.
Machine-assisted qualitative analysis (Towler et al.
, 2.
such as a We examined the potential to support healthcare interventions by comparing MATA with Auhuman-onlyAy thematic analysis techniques on the same dataset .
,472 user responses from a COVID-19 behavioral intervention refers to qualitative analysis that is aided by AIAos ability to classify or recognize patterns.
Digital discourse analysis (Vysquez, 2.
: Examining Meaning in Digital Communication While Considering Algorithms as Part of the Message Structure.
Epistemological Shift: The transition from observation to systemic interpretation is a critical step in the analysis of social phenomena.
Within the domain of AI, data does not merely reflect reality as it is, but rather, it is the consequence of systemic production involving algorithmic roles.
This indicates that knowledge derived from AI data is not solely the consequence of social interactions between humans but also the result of: Algorithmic filtering, prioritization based on machine learning, and calculative system logic.
Consequently, the epistemology of communication research must transition from a mere observation of reality to an understanding of how reality is produced by technological systems.
Ontological Shift: The following study will examine the phenomenon of non-human actors participating in communication.
Conventional research methodologies have historically operated under the assumption that humans are at the core of the communication process.
However, with the advent of AI, the ontology of communication has undergone a significant shift.
AI, manifesting as chatbots, recommendation systems, or automatically generated content, now functions as an agent in communication, despite its lack of human-like consciousness.
Consequently, researchers must expand the categories of research subjects and develop methodologies that also facilitate interaction with non-human entities.
Approaches such as Actor-Network Theory (Latour, 2.
Posthuman ethnography (Lotherington et al.
, 2.
, and Critical Algorithmic Studies (Guerra, 2.
provide a framework for exploring the relationships between humans, technology, and complex interconnected systems.
Taken together, these discussions demonstrate that AI cannot be explained through a single theoretical lens.
Instead, understanding AIAos communicative functions requires the convergence of interpersonal, mass communication, and technology-mediated This synthesis directly addresses the second research question by showing that theoretical boundaries within communication science can no longer operate in The convergence of these perspectives provides a more comprehensive foundation for examining how AI produces, mediates, and transforms meaning across diverse communication contexts.
Key Finding 3: Methodological.
Ethical, and Epistemological Disruptions When employing artificial intelligence in research, ethical and reflexivity issues emerge that necessitate serious consideration (Chatzichristos, 2.
raising significant epistemological and methodological, questions.
This study explores the dual potential of AI to enhance the scalability in qualitative research while challenging its interpretive depth.
situates this tension within the historical trajectory of qualitative research -and specifically Grounded Theory- from positivist to constructivist paradigms, highlighting how AIAos Jurnal ASPIKOM.
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P-ISSN: 2087-0442.
E-ISSN: 2548-8309
AI and communication research: Reassessing theory and methodology in the age of automation automated, data-driven approaches may signal a resurgence of positivist assumptions.
Key research questions guide this exploration: To what extent do qualitative researchers harness AIAos efficiencies in data analysis? Can the extended use of AI in qualitative research impact the depth and reflexivity essential to interpretive analysis? To delve into these questions the study employs a Technology Acceptance Model (TAM.
Maintaining transparency in AI-assisted research processes and evaluating whether data collected by AI systems can be considered representative of reality are critical concerns.
This study seeks to explore the position of the researcher within a system that is also shaped by algorithmic logic.
In this context, the scope of research ethics has expandedAinot only to protect human subjects but also to interrogate the boundaries of AIAos role in accessing, processing, and disseminating As a result, communication research methodology in the AI era must rest on three fundamental pillars.
Firstly, research systems must be adaptable to new forms of data, emerging communication patterns, and human-machine interactions.
Secondly, researchers must adopt a critical perspective on the power dynamics and ideological influences embedded in the design of AI systems and algorithms.
Thirdly, methodological approaches must be context-sensitive, allowing for meaningful connections between global developments in artificial intelligence and local cultural values, dynamics, and communication practices.
Thus, the paradigm has shifted from merely using AI in research to consciously conducting studies within a world increasingly shaped by AI logics and Overall, these insights show that AI disrupts not only communication practices but also the methodological, epistemological, and ethical foundations of communication research.
This finding answers the third research question by revealing that methodological decisions in AI-based research must account for algorithmic opacity, researcher positionality, and machine-driven meaning production.
The adoption of AI within the creative domain poses epistemological and moral dilemmas, as efficiency and personalization often overshadow the emotional nuance, intuition, and human-centered values essential to communication practices (Faustyna, 2.
As a result, communication research requires adaptive and reflective methodological frameworks capable of examining both human and non-human communicative agents.
Conclusion This study demonstrates that the integration of artificial intelligence (AI) into communication fundamentally shifts the fieldAos theoretical and methodological foundations.
The analysis shows three main transformations: first.
AI expands communication beyond human-centered models by acting as communicator, medium, and autonomous meaningmaker.
second, this development necessitates theoretical convergence across interpersonal, mass, and technology-mediated communication traditions.
and third.
AI introduces methodological, epistemological, and ethical disruptions that require researchers to reconsider how data, meaning, and agency are produced in digital environments.
The paper contributes to communication scholarship by offering an integrated framework that synthesizes these shifts, linking theoretical debates with methodological consequences in a way that has received limited attention in previous research.
This framework provides a foundation for developing adaptive and context-sensitive approaches to studying communication in AI-mediated settings.
Future research should deepen empirical and conceptual examinations of humanAemachine and machineAemachine communication, particularly by assessing how AI systems co-produce meaning, shape communicative Jurnal ASPIKOM.
Vol.
10 No.
P-ISSN: 2087-0442.
E-ISSN: 2548-8309
Prahastiwi Utari.
Pramana Pramana.
Eka Nada Shofa Alkhajar agency, and influence epistemic conditions in digital communication.
Such efforts are essential for advancing communication theory in an era where non-human actors increasingly participate in the processes that generate and circulate meaning.
Acknowledgements The authors would like to express their sincere gratitude to Universitas Sebelas Maret, particularly the Institute for Research and Community Service (LPPM), for providing financial support through the Research Group Capacity Strengthening Scheme (PKGRUNS) B.
This research was conducted under the auspices of the Research Group on Media.
Audience, and Socio-Cultural Systems, led by the Head of the Research Group, with contract number 371/UN27.
22/PT.
03/2025.
References