AI In the Creation of Marketing Communication Content

It is evident from the outcomes reviewed to date that analysis of demographic factors such as age and gender can contribute significantly to understanding how these variables affect AI familiarity and frequency of AI practice (Cai, 2024; Moravec et al., 2024; Armutat et al., 2024). For example, several studies suggest that older populations are less exposed to AI technologies and are often more wary of them, while younger audiences incorporate AI into their routine activities at a significantly faster rate (Kang, 2024). At the same time, it is highlighted that although gender is still a subject of investigation in the use of AI, the differences between men and women are gradually narrowing and individual interests or technological skills are more likely to come to the fore (Arısoy Gedik & Ceyhan, 2024). The question of the frequency and manner of the real use of AI can be raised not only in the marketing sphere, but also in education. In this field, automated assessment and performance analysis tools raise a number of ethical and practical questions (Bulut & Beiting-Parrish, 2024; Ghani et al., 2023). In particular, debates concern the accuracy of the systems and the privacy of the data used to personalize learning or marketing content (Mishara, 2024; Kozyreva et al., 2021). In addition, several authors point out that it is essential that this data is processed transparently, which strengthens user trust (Islam et al., 2024; Gungunawat et al., 2024). For marketing itself, it is important to further understand what the quality of AI- generated content is and to what extent consumers trust it. Available research shows that regular interaction with chat models or virtual assistants leads to perceptions of outputs as credible (Isaksen et al., 2025; Ghildyal et al., 2024). On the other hand, if users do not yet have a clear idea of what AI can do, they may approach its results with more skepticism (Levantis & Sgora, 2024). It is repeated experiences with AI systems that can increase many people's confidence in their ability to generate coherent and relevant content (Henestrosa & Kimmerle, 2025; Marquis et al., 2024). An equally interesting direction is to track where and in what form people commonly encounter AI (Balamurugan, 2024; Babadoğan, 2024). This may include generative text technologies, image processing algorithms, or augmented reality applications (Islam et al., 2024). Publications by Smith and Hutson (2024) and Patil (2025) suggest that content automation, for example in the creation of marketing campaigns, will continue to grow, while also highlighting the importance of exploring end-users' expectations: are they prepared to accept content without human correction or will they require a degree of control? The strategic implications that AI can have on the media and marketing sector are also relevant in the longer term. Several authors argue that AI technologies are streamlining the tailoring of content to different target audiences and changing traditional marketing strategies (Kubovics, 2024; Bobro et al., 2024; Raut et al., 2024). However, it remains open whether customers will fully accept automated solutions and to what extent they expect human supervision, especially in cases where more sensitive interactions are involved (Rao et al., 2025; Muvva, 2025). The aforementioned themes would be particularly useful to explore in terms of the impact of demographics on AI use, detailing the manner and frequency of interaction with AI tools, through to the longer-term impacts on marketing communications, thus capturing a more complete picture of how AI is reshaping the media and communications landscape. At the same time, these insights could fundamentally guide the further development of marketing practices, reflecting the need for transparency, ethical accountability and consideration of audience diversity.

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