AI Attribution Lowers College Students’ Trust in Sexual Health Information

College students who were told that identical sexual health passages came from generative artificial intelligence rated the information less favorably than students who were told the same passages came from a human source. The randomized experiment included 266 students from a single university in the southeastern United States and examined sexual communication, body image, and sexual pleasure and safety. Across perceived accuracy and clarity, warmth and helpfulness, trustworthiness, self-efficacy, and willingness to act, generative-AI attributions performed worse than academic-expert attributions. The difference was most pronounced for trustworthiness and willingness to act, the two measures most directly connected to whether participants felt able to rely on or follow the information. These results concern evaluations of identical text, not differences in the underlying information or evidence that any particular system produces consistently accurate answers.

The study began from an uneven information landscape. Sexual education requirements vary substantially across US states, and many provide minimal or no formal instruction. National monitoring cited by the researchers also indicates that only about half of college students report receiving education on topics such as refusal skills or consent communication. At the same time, generative AI systems such as ChatGPT, Claude, and Copilot can produce conversational responses from large collections of training text. Earlier assessments discussed in the article found that some systems generally answered questions about sexual violence, sexting, and sexual communication accurately, while other research has associated AI-generated material with perceptions of inaccuracy, limited human understanding, or emotional insensitivity. The authors therefore tested whether the identity attached to sexual health information affects how college students evaluate and respond to it.

Participants were recruited through the institutional email list of a large university in the southeastern United States between June and October 2025. Of 420 people who began the survey, 154 were excluded because of incomplete data or failed attention and manipulation checks, leaving an analytic sample of 266. Ages ranged from 18 to 59, with a mean of 22.06 years and a standard deviation of 5.71. Women made up 63.2% of the final sample. The investigators had planned four source conditions: generative AI, an academic expert, a community health professional, and a peer or social media influencer. After numerous participants confused the latter two descriptions, the researchers combined them for the primary analysis because they did not differ significantly on the study variables and both represented nonexpert human sources. A supplementary analysis retaining the original four conditions produced substantively unchanged results.

Each participant was randomly assigned to one of the four source descriptions and then read three passages of similar length and neutral tone. The passages addressed sexual communication, body image, and sexual pleasure and safety, all of which had been selected through a needs assessment previously conducted at the university. The source label was displayed before each passage, making attribution the experimental difference rather than the prose itself. In reality, every response had been generated by ChatGPT version 3.5. The design therefore isolated how respondents responded to the claimed author while holding the text constant. It did not compare independently authored human passages with independently produced AI passages, nor did it test the factual performance of several generative-AI models in ordinary use.

After each passage, participants rated the response on five dimensions using study-specific items. Perceived accuracy and clarity combined judgments about factual correctness, understandability, professional presentation, and reliability. Warmth and helpfulness covered whether the response seemed welcoming, empathetic, practical, and applicable to daily life. Trustworthiness included whether the source could be trusted, appeared unbiased and objective, and could be shared with a friend. Self-efficacy concerned confidence in applying the advice, making informed decisions, and using the information to improve well-being. Willingness to act combined the likelihood of following recommendations, considering a behavioral change, and sharing the response with a friend who sought advice. The researchers analyzed the ratings with five mixed analyses of variance, treating source condition as a between-subjects factor and topic as a repeated within-subjects factor.

Source attribution had a statistically significant main effect on every outcome. Academic-expert-attributed information received higher ratings than generative-AI-attributed information for all five measures. The combined community-professional and peer-influencer condition generally fell between the two, but it did not differ significantly from the academic-expert group on any outcome. For trustworthiness and willingness to act, the combined human condition also scored significantly higher than the generative-AI condition. For accuracy and clarity, warmth and helpfulness, and self-efficacy, however, the combined human group did not differ significantly from either the academic-expert or generative-AI groups. Ratings were above the midpoint of the five-point scales overall, showing that the participants generally evaluated the passages positively even when those passages were attributed to generative AI.

The source effect was generally similar across the three sexual health topics, but one interaction reached statistical significance. Source-by-topic interactions were not significant for accuracy and clarity, warmth and helpfulness, trustworthiness, or self-efficacy. The interaction for willingness to act was significant, though its partial eta squared was .022, indicating a modest variation. Topic-specific comparisons produced a distinct pattern. There were no significant source differences for sexual communication. For body image, the academic-expert condition was associated with greater willingness to act than either the generative-AI or combined community/peer condition, while the latter two did not differ significantly from each other. For sexual pleasure and safety, all three conditions differed significantly: generative-AI attribution produced the lowest willingness to act, and academic-expert attribution produced the highest. This was the only topic for which all three source groups were separated on that measure.

The source differences remained statistically significant in a robustness check using five one-way analyses of covariance. These analyses controlled for participants’ familiarity with and use of generative AI and for their attitudes toward using AI in education. Adding those measures did not meaningfully change the pattern of results. In statistical terms, the observed source effects were therefore not accounted for by the individual differences measured in this study. The analysis cannot identify the precise reason for the lower ratings, and the experiment did not directly test explanations involving empathy, lived experience, or other proposed mechanisms. It also should not be read as evidence that the participants’ responses generalize to people without college experience or to institutions outside the United States.

Several limitations constrain how the findings can be interpreted. All participants came from one southeastern university, and college students may be especially inclined to trust academic experts because of their familiarity with academic settings. The predominantly female sample may further limit generalizability if gender differences shape responses to trust or sexual-health information. The measures of AI familiarity, trust, and related perceptions were developed specifically for the study. Although the items had good internal consistency and were informed by earlier research, they had not undergone extensive psychometric validation. The content was also produced with an earlier version of ChatGPT, so the results may not characterize newer systems as their output quality and users’ familiarity change. Finally, the outcomes were perceived qualities and stated intentions rather than observed behavior, making it inappropriate to treat willingness to act as evidence of real-world behavioral change.

The evidence supports a narrow but notable conclusion: in this sample, the label attached to unchanged sexual health passages influenced how credible, useful, and actionable students considered them to be. It does not establish that generative AI is more or less accurate than a human expert across actual consultations, that AI-generated passages are unsuitable for sexual-health education, or that pairing the technology with human guidance will improve learning or behavior. Those questions require direct comparisons of generative-AI-only, human-only, and combined delivery systems, followed by measurement of actual and longitudinal outcomes. The present study also does not test whether a human endorsement affects misinformation susceptibility, whether students become more or less critical after learning that content was AI-generated, or whether source effects persist outside a college population. It instead shows that identical information can receive different evaluations when its attributed author changes, providing a reason to examine source presentation as one factor in future research on digital sexual-health communication.

Subject of Research: Source attribution effects on college students’ evaluations of sexual health information

Article Title: Generative AI vs. Human Sources for Sexual Health Information: Credibility, Trust, and Behavioral Intentions in a U.S. College Sample

Article References: Woerner, J., Marcantonio, T. L., & López Castillo, H. (2026). Generative AI vs. Human Sources for Sexual Health Information: Credibility, Trust, and Behavioral Intentions in a U.S. College Sample. Archives of Sexual Behavior. https://doi.org/10.1007/s10508-026-03594-z

Image Credits: AI Generated

DOI: 10.1007/s10508-026-03594-z

Keywords: generative AI, sexual health information, source credibility, trustworthiness, behavioral intentions, college students, randomized experiment, health communication, ChatGPT, sexual pleasure and safety, Generative, Human