International Journal of Advertising

AI in Advertising: New Directions in Practice, Consumer Responses, and Research Methods

Artificial intelligence (AI) is fundamentally reshaping advertising, not only as a technology that influences consumers, but as a collaborative partner embedded in how advertising is conceived, produced, and studied (Huh, Nelson, and Russel, 2023; Taylor, 2025). Generative AI has become a routine presence in advertising creative work, strategic planning, and campaign management (Cui, Liu, and Yuan, 2025). The emergence of agentic AI, systems capable of initiating, planning, and executing tasks with minimal human prompting, signals a further shift: from AI as a reactive tool to AI as a proactive force at every level of advertising practice and inquiry (Kim, 2025).

This transformation raises consequential questions that span the full advertising ecosystem. How are human creatives, strategists, and agencies working alongside AI systems (Yang et al., 2026), how are roles, accountability, and authorship being reconfigured in that process (Cui et al., 2025) and how does it impact creative agency of advertising professionals (Lee, 2022)? How is AI reshaping the ways consumers encounter, process, and respond to advertising (Chu, Yim, & Mundel, 2024; Kim and Wang, 2025; Rodgers, 2021)? And how should advertising researchers adapt their methods, standards, and ethical frameworks when AI becomes a partner in data collection, stimulus generation, and analysis? What ethical, privacy, and vulnerability-related safeguards are needed as AI systems personalize, simulate, and automate advertising interactions?

This special issue seeks contributions that advance theoretical, empirical, and methodological understanding of AI’s role across three interconnected domains:

Human-AI Collaboration (HAC) in Advertising examines how creatives, strategists, planners, and agencies collaborate with generative and agentic AI in content creation, campaign optimization, and brand communication (Feng and Sun, 2026), and how these partnerships reconfigure authorship, creative judgment, and governance (Ryoo et al., 2026).

AI and Consumer Response examines how AI-generated, AI-personalized, and AI-enabled advertising shapes consumer perception, attitudes, emotion, behavior, and well-being across diverse contexts, including commercial advertising, public service announcements, political communication, health communication, nonprofit campaigns, and public policy issues  (Campbell et al., 2022; Kietzmann, Paschen, & Treen, 2018; Mariani, Perez‐Vega, & Wirtz, 2022; Taylor, 2026; Xie, Muralidharan, and Phua, 2026). This includes privacy expectations and vulnerabilities among at-risk audiences (Strycharz & Segijn, 2022).

AI in Advertising Research examines how researchers collaborate with AI in ideation, stimulus generation, data collection, and analysis, and what standards of validity, reliability, replicability, and research ethics that collaboration demands (Puntoni et al., 2021; van Berlo, Campbell, and Voorveld, 2024). This includes issues of copyright and plagiarism, privacy protection of research participants, consent, and safeguards for vulnerable populations in AI-mediated research.

We seek contributions that move beyond documenting tools and practices toward enduring insights that speak to fundamental questions about advertising, persuasion, and communication. All methodological approaches are welcome, including experimental, survey-based, computational, qualitative, mixed-methods, and conceptual/theoretical papers.

Topics of interest include but are not limited to:

• Human-AI collaboration in advertising creative processes, including authorship, creative judgment, and practitioner roles across different contexts of advertising
• AI-assisted campaign strategy, optimization, and automated decision-making
• Agentic AI as a proactive partner in advertising practice and its implications for human oversight and accountability
• Governance structures for human-AI collaboration in advertising agencies and brand organizations
• The full range of human-AI collaborative modes, from tool use to co-creation to agentic delegation, and their implications for advertising practice and governance
• Human-AI collaboration in prosocial and public interest advertising

• Consumer perception, emotion, evaluation, and response to AI-generated advertising
• AI-driven personalization and its effects on persuasion, privacy, and consumer trust, consent, data protection, and consumer vulnerability
• Anthropomorphism, virtual influencers, AI digital twins, and AI agents as advertising vehicles
• AI advertising across diverse contexts, such as PSAs, health communication, political advertising, public policy, and nonprofit campaigns
• Ethical implications of AI advertising for vulnerable audiences, including children, older adults, patients, and politically targeted publics
• Consumer awareness, literacy, and resistance in response to AI-driven advertising systems

• Construct validity and stimulus equivalence in advertising experiments using AI-generated materials
• Reliability and replicability standards for AI-assisted advertising research
• Validity of AI-mediated data collection in advertising research
• Research ethics of AI-simulated participants and agentic data collection in advertising studies
• Privacy, consent, and data protection in AI-assisted research, synthetic data, and agentic data collection
• Bias and representational equity introduced through AI-assisted research designs and/or AI-generated advertising content
• Theoretical implications of human-AI collaboration for established advertising frameworks
• Agentic AI in advertising research and the governance structures needed to maintain scholarly rigor

References

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Chu, S. C., Yim, M. Y. C., & Mundel, J. (2024). Artificial intelligence, virtual and augmented reality, social media, online reviews, and influencers: a review of how service businesses use promotional devices and future research directions. International Journal of Advertising, 44(5), 798–828. 

Cui, W., Liu, M. J., & Yuan, R. (2025). Exploring the integration of generative AI in advertising agencies: A co-creative process model for human–AI collaboration. Journal of Advertising Research, 65(2), 167-189.

Feng, Y., & Sun, Y. (2026). Consumer empowerment through generative artificial intelligence: enhancing brand narrative with collaboration, creation, and communication. International Journal of Advertising, 45(1), 229-261.

Hudders, L., & Lou, C. (2022). The rosy world of influencer marketing? Its bright and dark sides, and future research recommendations. International Journal of Advertising, 42(1), 151–161.

Huh, J., Nelson, M. R., & Russell, C. A. (2023). ChatGPT, AI Advertising, and Advertising Research and Education. Journal of Advertising, 52(4), 477–482.

Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263-267.

Kim, J. (2025). Advertising in the age of agentic AI: Call for research. Journal of Interactive Advertising, 25(3), 215-221.

Kim, H., & Wang, Y. (Mia). (2025). Unveiling the human touch: how AI chatbots’ emotional support and human-like profiles reduce psychological reactance to promote user self-disclosure in mental health services. International Journal of Advertising, 1–25. 

Lee, H. K. (2022). Rethinking creativity: Creative industries, AI and everyday creativity. Media, Culture & Society, 44(3), 601-612.

Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755-776.

Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131-151.

Rodgers, S. (2021). Themed Issue Introduction: Promises and Perils of Artificial Intelligence and Advertising. Journal of Advertising, 50(1), 1–10.

Ryoo, Y., Bakpayev, M., Jeon, Y. A., Kim, K., & Yoon, S. (2026). High hopes, hard falls: Consumer expectations and reactions to AI-human collaboration in advertising. International Journal of Advertising,45(1), 48-80.

Strycharz, J., & Segijn, C. M. (2022). The future of dataveillance in advertising theory and practice. Journal of Advertising, 51(5), 574-591.

Taylor, C. R. (2025). six critical research needs on the use of artificial intelligence in advertising. International Journal of Advertising, 44(6), 981-984.

Taylor, C. R. (2026). The need for more research on public policy issues related to the use of artificial intelligence in advertising. International Journal of Advertising, 45(2), 263-265.

van Berlo, Z. M. C., Campbell, C., & Voorveld, H. A. M. (2024). The MADE Framework: Best Practices for Creating Effective Experimental Stimuli Using Generative AI. Journal of Advertising, 53(5), 732–753.

Xie, Q., Muralidharan, S., & Phua, J. (2026). How Women Respond to Computer-Generated Inclusive Advertising: Advocating for Body Positivity in the Age of AI. Journal of Advertising, 1–22. https://doi.org/10.1080/00913367.2026.2640989

Yang, J., Dong, C., Chu, S. C., & Rheu, M. (2026). Transforming advertising in the age of generative AI: Exploring advertising professionals’ perceptions of human-AI value co-creation. International Journal of Advertising, 45(1), 139-170.