What Makes Medical Students Trust AI Chatbots? New Study Reveals the Psychological Blueprint

Generative artificial intelligence chatbots have swept through university campuses with a speed that few educational technologies have ever matched, and medical schools are no exception. Students now routinely turn to these conversational systems for everything from summarizing dense textbook chapters to rehearsing differential diagnoses. Yet a fundamental question has lingered beneath the enthusiasm: what actually determines whether a medical student decides to keep using these tools? A new cross-sectional study from Hormozgan University of Medical Sciences in Iran offers one of the most detailed psychological maps to date, and its findings suggest that the answer lies not just in how useful a chatbot seems, but in how digitally literate and how secure students feel when they interact with it.

The research, published in BMC Medical Education, was led by Hesamaddin Kamalzadeh and colleagues, who set out to test whether the classic Technology Acceptance Model, or TAM, still tells the full story in the age of generative AI. TAM, developed decades ago to explain why people adopt new technologies, rests on two pillars: perceived ease of use, meaning how effortless a tool feels to operate, and perceived usefulness, meaning how much a person believes the tool improves their performance. These two perceptions shape a user’s attitude toward the technology, which in turn drives their behavioral intention to keep using it. The Iranian team extended this framework with two constructs they considered essential for the AI era: AI literacy, which measures how well a person understands what artificial intelligence can and cannot do, and perceived security and privacy, which captures how safe users feel sharing information with a chatbot.

To gather their evidence, the researchers distributed a structured online survey to 292 medical students at Hormozgan University between May and June 2026. Eligibility was deliberately restricted to students who had already used a generative AI chatbot for academic purposes, ensuring that responses reflected genuine experience rather than curiosity about an unfamiliar tool. Of the 292 students invited, 267 completed the survey, and after screening, 252 participants with prior academic use of GenAI chatbots formed the final analytical sample. The cohort had a mean age of 22.64 years with a standard deviation of 2.28, and 147 participants, or 58.3 percent, were female. All participants provided electronic informed consent through a mandatory confirmation item before taking part, and the study received ethics approval from the university’s committee under code IR.HUMS.REC.1404.273.

The analytical engine behind the study was partial least squares structural equation modeling, or PLS-SEM, a statistical technique widely used in behavioral research to test networks of hypothesized relationships between latent constructs that cannot be directly observed. Rather than measuring AI literacy or perceived usefulness with a single question, the researchers used multi-item questionnaire scales and then verified that these scales behaved as coherent, reliable measurements. The measurement model passed standard checks for reliability and validity, giving the team confidence that the constructs were being captured accurately before any structural relationships were tested.

The structural results revealed a striking asymmetry in how the two new constructs behaved. AI literacy showed a strong positive association with perceived ease of use, with a path coefficient of 0.479 and a p-value below 0.001, indicating that students who understood AI better found chatbots significantly easier to operate. Perceived security and privacy, by contrast, was positively associated with perceived usefulness, with a smaller but statistically significant coefficient of 0.157 and a p-value of 0.005. In other words, feeling safe with a chatbot did not make it feel easier to use, but it did make students believe the tool was more valuable for their academic work. This dissociation is one of the study’s most technically interesting findings, because it suggests that literacy and trust operate through entirely separate psychological channels rather than merging into a single attitude.

Downstream in the model, the classic TAM sequence held firm. Perceived ease of use and perceived usefulness both fed positively into students’ attitudes toward using generative AI, and attitude in turn showed the strongest single association with behavioral intention, with a path coefficient of 0.524 and a p-value below 0.001. This means that the emotional and evaluative stance a student holds toward chatbots, their attitude toward using GenAI, was a more powerful predictor of their intention to keep using these tools than either ease of use or usefulness alone. The model as a whole explained 38.7 percent of the variance in behavioral intention, with R-squared values ranging from 0.229 to 0.387 across the endogenous constructs, a level of explanatory power that is respectable for behavioral research in complex educational settings.

Why do these numbers matter beyond the statistics classroom? Medical education is a domain where the stakes of AI adoption are unusually high. A chatbot that helps a student draft a study plan is one thing; a chatbot that shapes clinical reasoning is quite another. If acceptance is driven partly by AI literacy, then students with a sophisticated understanding of how large language models work, including their tendency to hallucinate plausible but incorrect information, may be better positioned to use these tools critically rather than credulously. Conversely, students with low literacy might either reject the technology outright or embrace it uncritically, and the study’s finding that literacy boosts perceived ease of use suggests that training programs could lower the psychological barrier to adoption while simultaneously improving the quality of use.

The privacy finding carries its own practical weight. Medical students routinely handle sensitive clinical scenarios, and even in academic exercises, the material they type into a chatbot can touch on patient-like details. The study’s demonstration that perceived security and privacy strengthens perceived usefulness implies that institutional policies are not merely bureaucratic safeguards but active drivers of adoption. If a university clearly communicates how student data is handled, what is logged, and what protections exist, students may come to see the chatbot as more genuinely useful, creating a virtuous cycle in which trust and engagement reinforce each other. The authors conclude that strengthening AI literacy and establishing clear privacy and security policies may support the informed and responsible integration of generative AI chatbots into medical education.

It is worth noting the study’s design boundaries. As a cross-sectional survey conducted at a single institution, it captures associations at one point in time rather than proving causation, and its findings from one Iranian medical school may not generalize universally. The sample was also restricted to students with prior academic use of chatbots, so the model says nothing about what convinces a skeptic to try the technology in the first place. Still, the effect sizes and the coherent structure of the results give the findings real weight, and the extended TAM framework offers a template that other institutions can adapt to their own student populations.

What emerges from this research is a portrait of a generation that has already moved past the novelty phase of generative AI. These are not students dabbling with a trendy tool; they are future physicians whose daily workflows are being quietly reshaped by systems that can converse, summarize, and reason in natural language. The study suggests that the institutions that will integrate these tools most successfully are those that invest in two things simultaneously: teaching students how AI actually works, and guaranteeing, in policy as well as practice, that their data is protected. Attitude, the study shows, is the fulcrum on which adoption turns, and attitude is built from both competence and confidence. As generative AI continues its march through medicine, the psychological blueprint uncovered in Bandar Abbas may prove to be a surprisingly universal map.

Subject of Research: Psychological and technological factors influencing medical students' acceptance of generative AI chatbots in medical education

Article Title: Factors associated with generative AI chatbot acceptance among medical students with prior academic use: a cross-sectional study based on an extended technology acceptance model

Article References: Kamalzadeh, H., Boushehri, E., Hayavi-Haghighi, M. H., Salari, M. S., & Abedini, S. (2026). Factors associated with generative AI chatbot acceptance among medical students with prior academic use: a cross-sectional study based on an extended technology acceptance model. BMC Medical Education. https://doi.org/10.1186/s12909-026-10647-3

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10647-3

Keywords: generative AI, chatbots, medical education, Technology Acceptance Model, AI literacy, data privacy, medical students, PLS-SEM, behavioral intention, health informatics, cross-sectional study, Iran