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This editorial introduces a special issue of Human-Machine Communication that explores how generative AI reshapes the communicative relationship between humans and machines. It highlights emerging research on technology use, education, interpersonal dynamics, and trust in AI-generated content, emphasizing that generative AI’s significance lies not in novelty but in the social negotiations it provokes around meaning, authority, and credibility.

People’s interaction with online content is increasingly facilitated by intelligent user interfaces and artificial agents. In this article, I explore this shift by drawing on ethnographic fieldwork with users of the TikTok app. More specifically, I write on their interactions with the TikTok algorithm as a form of human–machine interaction and through the lens of trust. Along concrete ethnographic data, this article lays out the multifaceted process in which participants negotiated trust in the TikTok algorithm as an interaction partner in their everyday pursuits for relaxation and entertainment. Understanding trust as something deeply relational, mediating the position that one takes to another, this article outlines the constitutive embodied and affective dimensions of trust. It shows how participants dealt with feelings of their trust in the TikTok algorithm being put to the test, as well as how they negotiated their distance and closeness to it accordingly. By doing so, this article will demonstrate how trust functions a key mediator of meaningful human–machine interaction – shaping not just meaningful outcomes but also meaningful processes of interaction. From this angle, this article closes with an argument for research on the foundational role of trust in human–machine interaction, specifically in ways that look beyond the cognitive processes of judging trust and broadening the scope towards the material and cultural contexts in which people trust others.

This introduction traces the emergence of Human-Machine Communication (HMC) as a distinct field centered on communication across ontological boundaries. Arguing that HMC is not merely about interacting with machines but about how communicative presence and legitimacy are constituted, the authors introduce the Act–Mean–Relate (AMR) paradigm to conceptualize how machines’ capacity for action, signification, and relation enable communication. HMC challenges human exceptionalism by shifting focus from internal states and categorical differences to symbolic and relational dynamics. It addresses the interpretive labor involved in making machine others intelligible and emphasizes communication as the co-construction of social reality. The article highlights tensions between computational and relational logics and treats design as a third party in communicative triads. In doing so, it frames HMC as a pluralistic, trans-ontological field that advances communication theory while attending to the ethical, epistemic, and material implications of human-machine communication.

The adoption of generative artificial intelligence (GAI) applications has bolstered efforts toward human-machine collaboration. Given the lag in research on AI and religion, this study examines how pastors engage GAI to develop religious human-machine communication practices that constitute their leadership. Findings from in-depth interviews with pastors in the US reveal that they view GAI as an idea generator, research assistant, co-author, and translator. Clergy enact multiple ways to incorporate GAI communication in religious education and to enhance sermonic performances. Concurrently, pastors perceive tensions between innovation and established rites, as they contend with the authenticity and spiritual depth of GAI content while meeting the needs of their congregants amid temporal and resource challenges. This article concludes with implications for future research, AI governance, and ethics.

Student use of GenAI is growing and so are the faculty concerns. With research providing mixed suggestions and approaches, this study sought to understand the student perspective on ethical uses, their own motives and perceived benefits of GenAI use, the risks involved, and their perceptions of susceptibility and severity related to unethical use (i.e., plagiarism/cheating). Results revealed the complexity of student considerations regarding the uses, risks, and benefits of GenAI and its potential for personalized learning enhancement. Students generally view the likelihood of being caught submitting AI-generated work as their own as high and the consequences as severe. The viability of applying persuasive theory to ethical decision-making and HMC, as well as implications of interweaving examinations of instructor-student communication and HMC are discussed.

Increasing ubiquitous collaborative intelligence between humans and machines requires human–machine communication (HMC) that is more human and less machine‐like to accomplish given tasks. Although speech signals are considered the best modes of communication in HMC, background noise often interferes with these signals. Therefore, research focused on integrating lip‐reading technology into HMC has gained significant attention. However, lip‐reading functions effectively only in well‐lit environments. In contrast, HMC may occur daily in dark environments owing to potential energy shortages, increased exploration in darkness, nighttime emergencies, etc. Herein, a possible method for HMC in the dark mode is presented, which is realized based on deep learning motion patterns of persistent luminescence (PL) of the skin surrounding the lips. An ultrasoft PL–polymer composite patch is used to record the motion pattern of the skin during speech in the dark. It is found that visual geometric group network (VGGNET‐5) and residual neural network (ResNet‐34) could predict spoken words in darkness with test accuracies of 98.5% and 98.75%, respectively. Furthermore, these models could effectively distinguish similar‐sounding words such as “around” and “ground.” Dark‐mode communication can allow a wide range of people, including disabled people with limited dexterity and voice tremors, to communicate with artificial intelligence machines. A possible method for human‐machine communication in the dark can be realized based on deep learning motion patterns of persistent luminescence of the skin surrounding the lips. It is found that deep convolutional neural networks can predict spoken words in the darkness with ≈98.5% accuracy. Dark‐mode communication can be useful for all people seeking to communicate with artificial intelligence machines.

The computers are social actors framework (CASA), derived from the media equation, explains how people communicate with media and machines demonstrating social potential. Many studies have challenged CASA, yet it has not been revised. We argue that CASA needs to be expanded because people have changed, technologies have changed, and the way people interact with technologies has changed. We discuss the implications of these changes and propose an extension of CASA. Whereas CASA suggests humans mindlessly apply human-human social scripts to interactions with media agents, we argue that humans may develop and apply human-media social scripts to these interactions. Our extension explains previous dissonant findings and expands scholarship regarding human-machine communication, human-computer interaction, human-robot interaction, human-agent interaction, artificial intelligence, and computer-mediated communication.

In human-machine communication, people interact with a communication partner that is of a different ontological nature from themselves. This study examines how people conceptualize ontological differences between humans and computers and the implications of these differences for human-machine communication. Findings based on data from qualitative interviews with 73 U.S. adults regarding disembodied artificial intelligence (AI) technologies (voice-based AI assistants, automated-writing software) show that people differentiate between humans and computers based on origin of being, degree of autonomy, status as tool/tool-user, level of intelligence, emotional capabilities, and inherent flaws. In addition, these ontological boundaries are becoming increasingly blurred as technologies emulate more human-like qualities, such as emotion. This study also demonstrates how people's conceptualizations of the human-computer divide inform aspects of their interactions with communicative technologies.

We argue for an interdisciplinary approach that connects existing models and theories in Human–Robot Interaction (HRI) to traditions in communication theory. In this article, we review existing models of interpersonal communication and interaction models that have been applied and developed in the contexts of HRI and social robotics. We argue that often, symmetric models are proposed in which the human and robot agents are depicted as having similar ways of functioning (similar capabilities, components, processes). However, we argue that models of human–robot interaction or communication should be asymmetric instead. We propose an asymmetric interaction model called AMODAL-HRI (an Asymmetric MODel of ALterity in Human–Robot Interaction). This model is based on theory on joint action, common robot architectures and cognitive architectures, and Kincaid’s model of communication. On the basis of this model, we discuss key differences between humans and robots that influence human expectations regarding interacting with robots, and identify design implications.

This study investigates the effects of task demonstrability and replacing a human advisor with a machine advisor. Outcome measures include advice-utilization (trust), the perception of advisors, and decision-maker emotions. Participants were randomly assigned to make a series of forecasts dealing with either humanitarian planning (low demonstrability) or management (high demonstrability). Participants received advice from either a machine advisor only, a human advisor only, or their advisor was replaced with the other type of advisor (human/machine) midway through the experiment. Decision-makers rated human advisors as more expert, more useful, and more similar. Perception effects were strongest when a human advisor was replaced by a machine. Decision-makers also experienced more negative emotions, lower reciprocity, and faulted their advisor more for mistakes when a human was replaced by a machine.