AI Literacy Emerges as a Shield Against Misplaced Trust in AI-Generated Social Media Content

Every scroll through a social media feed now carries a hidden question: was this made by a human? A new study from Jordan suggests that the more people encounter AI-generated content online, the less they trust it—and the more determined they become to check it before believing or sharing. Yet the research also reveals a troubling twist: trust itself can act as a trap, lulling users into skipping verification entirely, while AI literacy appears to loosen that trap’s grip.

The study, published in Frontiers in Human Dynamics by researchers at Jadara University, surveyed 1,150 social media users across northern, central, and southern Jordan. Using a statistical technique called partial least squares structural equation modeling, or PLS-SEM, the team mapped how exposure to AI-generated social media content—captions, images, videos, voiceovers, synthetic faces, and automated comments—shapes two psychological outcomes: trust in such content and the intention to verify it before acting on it.

The findings paint a coherent picture of digital skepticism. Exposure to AI-generated content was negatively associated with trust, meaning the more users saw of it, the less they believed it. But exposure was positively associated with verification intention—the willingness to fact-check, cross-reference sources, and confirm whether something was machine-made before accepting or resharing it. Trust, in turn, worked in the opposite direction: the more users trusted AI-generated content, the less inclined they were to verify it.

This inverse relationship between trust and verification is the study’s most striking insight. Drawing on the elaboration likelihood model and dual-process theories of cognition, the researchers argue that trust functions as a cognitive shortcut. When users perceive content as credible, reliable, or professionally produced, they feel less need to scrutinize it. In a fast-paced, attention-scarce social media environment, that shortcut can be dangerous—especially when AI-generated media is designed to look polished and authoritative even when it is fabricated or misleading.

The statistical model also uncovered a significant indirect pathway. Because exposure reduced trust, and reduced trust increased verification intention, the product of these two negative relationships yielded a positive indirect effect: more exposure ultimately led to more verification, routed through diminished trust. The researchers emphasize this is a statistical association, not proof of causation, since the data were collected at a single point in time from a self-selected sample.

But the most consequential finding involves AI literacy—the knowledge, skills, and critical competencies that allow people to understand how AI systems work, recognize their limitations and biases, and evaluate their outputs. AI literacy was directly and positively associated with verification intention. More importantly, it moderated the trust-verification relationship: among users with low AI literacy, the negative link between trust and verification was strongest (β = −.393), while among those with high AI literacy, it weakened considerably (β = −.147). In other words, people who understand AI are less likely to let trust alone dictate whether they check a claim.

The researchers ground their theoretical framework in Sundar’s MAIN model, which holds that users judge digital credibility through cues of modality, agency, interactivity, and navigability. When the agency behind a piece of content is ambiguous—human, machine, or a hybrid of both—users face uncertainty about its reliability. That uncertainty, the study suggests, is precisely what drives both eroding trust and rising vigilance. The findings also echo prior work showing that AI-synthesized faces can be indistinguishable from real ones and sometimes perceived as more trustworthy, raising the stakes for verification behavior.

The sample itself deserves scrutiny. Respondents were recruited through university-centered channels, with 74.8 percent reporting bachelor’s-level education and a majority aged between 18 and 26. Daily social media use was substantial: nearly 60 percent reported three or more hours per day, with TikTok and Instagram as the dominant platforms. The authors are explicit that these are associations from a convenience sample, not causal effects, and that results should not be generalized to all Jordanian social media users, let alone populations elsewhere.

Practically, the study carries weight for platform design and education. The authors suggest that clear AI-disclosure labels, provenance indicators, and timely verification reminders could support user evaluation—though they caution that poorly designed warnings risk fatigue, habituation, or false reassurance. They also argue for embedding AI literacy into media education, teaching students to interrogate synthetic text, images, and video rather than accept them at face value. As generative AI becomes woven into everyday communication, the study suggests that the ability to question machine-made content may be as fundamental as reading itself.

The distinction between verification intention and actual verification behavior deserves careful attention when interpreting these findings. The study measured what respondents said they intended to do when confronted with suspicious content, not what they actually did in real browsing sessions. Behavioral research has long documented a gap between stated intentions and enacted behavior, particularly in digital environments where checking a claim requires interrupting the momentum of scrolling. A user may genuinely intend to verify a suspicious video yet continue scrolling because the platform’s design rewards continued engagement rather than deliberate evaluation. This gap does not invalidate the findings, but it means the results speak most directly to the psychology of judgment rather than to observable fact-checking behavior, and future work using behavioral traces or experimental designs would be needed to confirm that intentions translate into action.

The mediation structure uncovered in the model also offers a useful lens for thinking about how skepticism develops. Rather than exposure directly prompting verification, the pathway runs through trust: repeated encounters with AI-generated content appear to erode the confidence users place in such material, and that erosion is what motivates scrutiny. This suggests that skepticism toward synthetic media is not simply a reflex but a learned calibration, built up through accumulated experience with content that may be polished yet unreliable. It also implies a potential vulnerability in the opposite direction. If exposure to AI-generated content were experienced as consistently accurate and helpful, the same mediating mechanism could operate in reverse, strengthening trust and thereby weakening the impulse to verify. The model’s logic therefore cuts both ways, which is one reason the authors frame trust as a double-edged factor in the information environment.

The moderating role of AI literacy carries implications that extend beyond individual behavior. If users with stronger AI literacy are less dependent on trust when deciding whether to verify, then disparities in literacy may translate into disparities in resilience against synthetic misinformation. Populations with limited exposure to AI concepts, formal instruction, or hands-on experience with generative tools may be left relying heavily on intuitive credibility cues—the very cues that synthetic media is engineered to exploit. This makes AI literacy not merely a technical skill but a component of informational equity, with potential relevance for educational policy, public communication campaigns, and platform-level interventions aimed at narrowing gaps in digital competence.

The Jordanian setting adds contextual value to the findings. Social media penetration in the country is high, and platforms such as TikTok and Instagram dominate daily use among younger cohorts, mirroring patterns seen across much of the Middle East and North Africa. Much of the existing research on misinformation and AI perception has been conducted in Western samples, so evidence from a non-Western, Arabic-speaking population helps address a longstanding imbalance in the literature. At the same time, the university-centered recruitment means the sample skews toward younger, more educated respondents, who may differ systematically from the broader population in both AI familiarity and skepticism. Older users, rural users, and those with less formal education remain underrepresented, leaving open the question of whether the same trust-verification dynamics hold across the full demographic spectrum.

The measurement of exposure itself is worth considering. Respondents self-reported how often they encountered AI-generated content, yet research on synthetic media detection shows that people frequently fail to recognize AI-generated material when they see it. Some content users believed to be human-made may in fact have been AI-assisted, and some content they labeled as AI-generated may not have been. This measurement uncertainty introduces noise that could either inflate or deflate the observed associations. It also highlights a practical challenge for the field: as AI-assisted editing becomes embedded in ordinary content creation tools, the boundary between AI-generated and human-generated content grows increasingly blurry, complicating both self-reports and future research designs.

Finally, the findings arrive at a moment when generative tools are becoming default features of mainstream platforms rather than novelties. Automated captions, AI-suggested replies, synthetic voiceovers, and algorithmically remixed video are now routine elements of the feed. If routine exposure genuinely cultivates verification habits, platforms may find that their users gradually develop collective antibodies to synthetic deception. But if trust in AI content stabilizes or recovers as the technology improves, the protective pathway identified in this study could weaken. Longitudinal research tracking the same users over time would be needed to determine whether the skepticism documented here deepens, plateaus, or fades as AI-generated content becomes indistinguishable from the ordinary fabric of social media life.

Subject of Research: How exposure to AI-generated social media content affects trust and verification intention, and the moderating role of AI literacy

Article Title: The influence of exposure to AI-generated social media content on verification intention: the mediating effect of trust and the moderating effect of AI literacy

Article References: Al-Mugheed, A. T., & Hatamleh, I. H. M. (2026). The influence of exposure to AI-generated social media content on verification intention: the mediating effect of trust and the moderating effect of AI literacy. Frontiers in Human Dynamics, 8, Article 1927700. https://doi.org/10.3389/fhumd.2026.1927700

Image Credits: AI Generated

DOI: 10.3389/fhumd.2026.1927700

Keywords: AI-generated content, social media, trust, verification intention, AI literacy, misinformation, PLS-SEM, Jordan, digital literacy, deepfakes, media credibility, generative AI