Cogent Social Sciences

AI-Mediated Persuasion and Audience Control in Strategic Communication

The contemporary information environment is no longer shaped solely by human communicators. Artificial intelligence now determines which messages audiences see, how those messages are emotionally framed, and when they are delivered, operating at a scale and speed that fundamentally outpaces human awareness. Algorithmic targeting, generative content, virtual influencers, and large language models have transformed advertising and strategic communication into a domain of persuasion that is simultaneously more powerful and less visible than at any prior moment in media history. Yet audiences are rarely equipped, cognitively, practically, or legally, to recognize, evaluate, or resist these systems. The result is a growing asymmetry between the sophistication of AI-driven persuasion and the ability of individuals and institutions to understand or govern it. This Article Collection examines that asymmetry directly, asking how audiences cope with AI-mediated persuasion, what happens when they cannot, and what scholars, practitioners, and policymakers can do about it.

This collection addresses one of the most consequential gaps in contemporary advertising and strategic communication research: the erosion of meaningful audience agency in the face of AI-driven persuasion systems. Disclosure policies remain inconsistent and poorly understood. Platform design frequently obscures rather than illuminates persuasive intent. Virtual spokespersons and AI-generated content blur the boundary between authentic communication and synthetic influence, often without audiences' awareness. Without rigorous analysis and updated frameworks, consumers risk persistent manipulation, and strategic communicators risk operating without ethical or regulatory guardrails suited to the environments they inhabit. This collection provides a critical forum for scholars and practitioners to examine how audiences understand and respond to AI-mediated persuasion, how disclosure and literacy interventions can restore meaningful agency, and how legal and ethical frameworks must evolve to protect audiences in an era defined by algorithmic influence. It invites multidisciplinary contributions that are both theoretically grounded and policy-relevant. In essence, this collection aims to generate actionable outputs, including empirically grounded disclosure frameworks, replicable literacy intervention models, and policy-ready guidelines that equip researchers, regulators, and industry practitioners to design more transparent and equitable AI-mediated communication environments. The collection welcomes theoretically grounded empirical studies, both quantitative and qualitative, as well as systematic reviews and computational analyses that advance our understanding of AI-mediated persuasion and its implications for audiences, communicators, and regulators.

This Collection examines how audiences understand, evaluate, and seek to control AI-mediated persuasion in strategic communication contexts. Topics include:

• Persuasion Knowledge in AI-Driven Advertising: Explores how foundational persuasion knowledge frameworks apply, and where they break down, in algorithmic and generative AI advertising environments.
• AI Disclosure Design and Regulation: Analyzes the effects of disclosure language, format, and placement on audience recognition, trust, and advertising credibility, including emerging regulatory frameworks.
• Recognition of AI-Generated Content and Virtual Influencers: Investigates how audiences identify, or fail to identify, synthetic media, AI-generated brand content, and virtual spokespersons.
• Algorithmic Transparency and Consumer Trust: Examines how platform opacity affects consumer confidence, perceived fairness, and willingness to engage with algorithmically delivered advertising.
• Personalization, Psychological Targeting, and Manipulation: Assesses the ethical boundaries of AI-driven message personalization, including profiling, microtargeting, and emotionally adaptive content.
• Parasocial Relationships with AI Agents and Virtual Influencers: Considers how audiences form emotional bonds with synthetic communicators and the strategic and ethical implications of those relationships for brand communication.
• AI and Advertising Literacy Across Populations: Reviews literacy interventions and their effectiveness across demographic, generational, and cultural groups, with attention to digital equity.
• Children and Adolescents in AI-Mediated Advertising Environments: Evaluates the particular vulnerabilities of younger audiences and the adequacy of existing protections in AI-rich media contexts.
• Elderly Consumers and Anthropomorphic AI Systems: Examines how emotionally designed and human-like AI agents affect older consumers' susceptibility to persuasion and their capacity for informed response.
• Deepfakes and Synthetic Media in Commercial Communication: Assesses the legal, ethical, and communicative implications of AI-generated audiovisual content in advertising and strategic communication.
• Platform Governance and Audience Agency: Surveys institutional, regulatory, and industry responses to AI-mediated persuasion, including self-regulatory frameworks and international policy initiatives.
• Cross-Cultural Dimensions of AI Persuasion: Explores how cultural context shapes audience awareness of, responses to, and protections against AI-driven strategic communication.

When submitting to this Collection, please select the "Media and Communications Studies Section," as well as the Collection's title, when prompted.

Dr. Ghanem Elhersh is an Assistant Professor of Social Media in the Department of Media and Communication at Stephen F. Austin State University. His research examines how emerging technologies, particularly artificial intelligence, shape audience engagement, strategic communication, and meaning-making across digital platforms, using mixed-methods and computational approaches.

Dr. Laeeq Khan is an Associate Professor in the School of Media Arts & Studies at Ohio University's Scripps College of Communication, where he also serves as Graduate Director. He directs the SMART Lab, a research group focused on quantitative and computational approaches to media and communication. His work sits at the intersection of data-driven methods and media, with ongoing projects examining topics such as audience engagement, health, and artificial intelligence. In addition to his research, Khan is actively involved in doctoral training and graduate program development, overseeing admissions, advising, and curriculum coordination across the program's quantitative and qualitative research tracks.

The Guest Advisors do not have any conflicts of interest to disclose.

For more information on this Collection please reach out to the Commissioning Editor, Dr. Molly Cole, at [email protected].