We present a proposed methodological model for evaluating content created by artificial intelligence

Generative artificial intelligence is becoming a natural part of the media and marketing landscape. Today, text, visuals, videos, and other forms of digital content are increasingly being created with the support of artificial intelligence systems. However, this also increases the need to systematically examine how such content is created, how people perceive it, and what influences their trust in its quality, authenticity, and credibility.

A research project focused on the creation and reception of AI-generated content addresses these questions. One of its interim outputs is a proposed methodological model that links the results of existing research with practical tools for evaluating AI content. The model is intended to serve as a framework for better understanding why certain AI-generated content appears credible, while other content evokes uncertainty, distrust, or rejection.

The model is based on research into trust, risk, and the acceptance of AI-generated content

The proposed methodological model is based on several research phases focused on the perception of artificial intelligence in the media and marketing environments. The research showed that the evaluation of AI-generated content cannot be reduced solely to its technical quality, grammatical correctness, or visual quality. An important role is also played by who receives the content, the context in which they encounter it, the risks they associate with it, and the extent to which they trust it.

The core of the model is therefore the relationship between perceived usefulness, perceived risk, and trust. In this model, trust acts as a key mediating factor between the content itself and the subsequent behavior of users. In other words, it is not enough to examine only whether AI-generated content is of high quality. It is equally important to understand whether the recipient considers it useful, safe, transparent, and trustworthy.

The model is based on the following core principles: perceived usefulness and risk, trust, acceptance, and user behavior. This framework makes it possible to examine how users’ attitudes translate into their willingness to read, share, use, recommend, or reject AI-generated content.

The methodological model is not a single indicator, but an interconnected system

The proposed methodological model is not based on a single universal score. It is an interconnected system of multiple frameworks that allow for the evaluation of AI-generated content from various perspectives. The model integrates the analysis of the content itself, target audiences, trust, experimental validation, and ethical boundaries. Its goal is to create a structured approach to examining AI-generated content not only in terms of how it looks, but also in terms of how it functions in communication with the audience.

The model consists of several interconnected parts:

1. Content Analysis

The first part of the model focuses on the content itself. It examines its quality, clarity, accuracy, originality, informational value, degree of automation, and suitability for a specific communication context. The goal is to identify which characteristics of the content may influence how users perceive it.

2. The 5P/6V Framework

The second part of the model uses the 5P/6V methodological framework, which serves to systematically structure the evaluation of AI-generated content. The framework allows for the assessment of content across multiple layers—from its purpose and creation process, through its target audience and platform, to evaluation criteria related to credibility, visibility, usability, impact, and verifiability. As a result, the model examines not only the final content but also the broader conditions of its creation and use.

3. The Anatomy of Trust

A key component of the model is the analysis of trust. This examines which factors increase or decrease people’s willingness to trust content created by artificial intelligence. Trust is understood here as a dynamic relationship between the content, its labeling, context, source, user experience, and perceived risk. The model therefore makes it possible to track whether a user trusts the content because of its quality, the brand that published it, transparent labeling, or their own experience with similar content.

4. Audience Segmentation

Another part of the model is based on the premise that different groups of users do not perceive AI-generated content in the same way. Casual and heavy users of digital tools, experts, students, marketing professionals, and people with higher levels of concern about misinformation evaluate it differently. Audience segmentation makes it possible to examine differences between groups and better understand which factors influence the acceptance of AI-generated content among specific types of recipients.

5. Experimental Verification

The model also incorporates experimental testing. This makes it possible to compare users’ reactions to human-generated content and AI-generated content, examine the impact of labeling AI content, test differences between various formats, and track changes in trust, attention, interest, and willingness to accept the content. The experimental component is particularly important because it allows the model’s assumptions to be verified using data, rather than solely at a theoretical level.

6. Ethical and Interpretive Limits

The final section of the model addresses ethical issues and the limits of interpreting results. The evaluation of AI-generated content cannot be separated from topics such as transparency, labeling, copyright, manipulation, disinformation, user protection, and the responsibility of creators and organizations. The model therefore addresses not only the question of whether the content is effective, but also whether its use is responsible and appropriate.

Five Paradoxes in the Perception of AI Content

The model also incorporates research findings that show perceptions of AI-generated content are often mixed. For example, users may rate AI-generated text as professional, yet at the same time less interesting or less engaging. Labeling content as AI-generated does not necessarily lower its overall rating, but it can change the way users interpret its origin and credibility.

At the same time, research shows that concerns about disinformation are very strong, but they do not necessarily directly predict trust in or acceptance of specific content. People often worry about the impact of AI-generated content on society in general, but perceive themselves as less vulnerable. Similarly, more extensive experience with AI may reduce the perceived risk, but it does not necessarily reduce the demand for regulation and transparency.

Another important finding concerns the recognition of AI-generated content. Without clear labeling, people are often unable to reliably determine whether content was created by a human or by artificial intelligence. Therefore, labeling in the model serves not merely as formal information, but as an important guide that helps users interpret the content.

The goal of the model is to bridge the gap between research and practice

The proposed methodological model is intended to serve as a basis for further research validation and practical application. In an academic setting, it can assist in designing experiments, analyzing data, and interpreting results. In media and marketing practice, it can serve as a guiding framework for evaluating content created by artificial intelligence, establishing rules for its use, or assessing its suitability for specific target audiences.

The model also promotes a more open approach to communicating research findings. Through the web-based research platform, individual frameworks, findings, and analytical sections documenting the development of the methodological approach are gradually being made available. The public can thus follow not only the research results but also the way in which the methodological model is gradually taking shape, being tested, and refined.

An Open Framework for Further Development

The proposed methodological model is not a definitive solution. It is an open framework that will continue to evolve based on new research findings, experiments, and feedback from professional practice. Its aim is not to replace critical evaluation of content, but to offer a systematic way to think about AI-generated content, analyze it, and better understand its effects on users.

At a time when artificial intelligence is becoming a common part of digital communication, this methodological approach is important not only for researchers but also for the media, marketers, content creators, students, and the general public. It helps identify the factors that influence trust, acceptance, and the responsible use of content created by artificial intelligence.

This article was produced as part of Project No. 09I03-03-V04-00367, supported by the European Union through NextGenerationEU funding as part of the Recovery and Resilience Plan of the Slovak Republic.