The survey, led by R. A. Higgoda, A. A. Alahakoon and J. Indrakumar, captured responses from 400 undergraduates spanning both the pre-clinical and clinical phases of Sri Lanka’s medical curriculum, with women making up 65.8 percent of the sample. It received ethics approval from the Faculty of Medical Sciences at Sri Jayewardenepura and was carried out in accordance with the Declaration of Helsinki, with informed consent obtained from every participant. Published online on 29 August 2026 as an open-access research article, the study set out to measure five intertwined dimensions of the chatbot phenomenon: what students actually know about these tools, how often they use them, how they judge their effectiveness and trustworthiness, which barriers stand in their way, and what they expect from AI as their careers unfold. The authors note that evidence on this question from Sri Lanka has so far been limited — a gap that matters, because most of what the medical-education community knows about chatbot adoption comes from wealthier countries whose students, infrastructure and assessment cultures differ in important ways.

Methodologically, the team relied on an instrument built for the local context rather than an imported questionnaire. The survey was piloted among Sri Lankan students before deployment, and its internal consistency — the degree to which items intended to measure the same underlying attitude produce aligned answers — was quantified with Cronbach’s alpha, a statistic running from zero to one that educational researchers conventionally treat as acceptable above 0.7 and excellent above 0.8. This instrument scored 0.856, signalling strong reliability. The researchers then moved beyond raw percentages into exploratory univariable binary and ordinal logistic regression, reporting unadjusted odds ratios (ORs) with 95 percent confidence intervals. Ordinal logistic regression is the natural tool when the outcome is a ranked scale — students rating chatbot effectiveness from poor to very effective, for example — because it models the odds of landing in a higher rather than a lower category. Crucially, the team tested the proportional-odds assumption, the premise that a predictor’s effect is identical across every cut point of the ranked outcome, and withheld common odds ratios wherever that assumption broke down — a conservative choice that trades statistical tidiness for honesty about what the data can cleanly support.

The perception data painted an overwhelmingly positive picture, edged with caution. Two-thirds of respondents — 67.5 percent — rated chatbots as effective or very effective learning aids, while 79.3 percent judged them trustworthy and 85.8 percent said the tools had improved their learning experience. Asked what the technology actually delivered, students converged on three benefits: 81.7 percent cited time savings, 81.5 percent pointed to constant availability — an always-on tutor that never sleeps, cancels a session or charges by the hour — and 71.7 percent valued personalised learning, the ability to interrogate a topic at one’s own pace, in one’s own words, as many times as needed. These advantages map directly onto the structural pressures of medical school, where dense syllabi, high-stakes multiple-choice and essay examinations, and limited access to one-on-one teaching make scalable, individualised support disproportionately valuable. For any student juggling those demands, the appeal of a tireless, on-demand digital study partner is not a gadget-lover’s whim; it is a rational response to genuine academic pressure, and the near-universal uptake figures suggest students have priced that trade-off accordingly.

Yet the warning flags flew high alongside the applause. Fully 76.2 percent of students identified inaccurate information as a major drawback — the most widely reported concern in the study — followed by reduced critical thinking at 66.9 percent and overdependence at 49.6 percent. The accuracy worry has solid technical grounding. Modern chatbots are built on large language models, neural networks trained on vast text corpora to predict plausible sequences of words. Because such models optimise for linguistic coherence rather than factual verification, they can generate confident-sounding but false statements — the phenomenon known as hallucination — including invented drug doses, distorted mechanisms of disease or fabricated references that look impeccable on the page. In a discipline where a wrong fact can eventually become a wrong prescription, that failure mode is not an inconvenience; it is a hazard. The critical-thinking concern cuts just as deep, because educators have long argued that outsourcing synthesis to a machine short-circuits the productive struggle through which students genuinely build clinical reasoning. And with almost half the cohort voicing fear of overdependence, the students themselves seem to sense that a helpful crutch can quietly harden into a cognitive cast.

The sharpest statistical divides separated pre-clinical from clinical students. In models that satisfied the proportional-odds assumption, clinical-year students — those already rotating through wards and clinics — reported significantly greater perceived effectiveness (OR 1.88, 95 percent CI 1.29–2.73, p = 0.001), a better overall learning experience (OR 1.97, 95 percent CI 1.35–2.88, p < 0.001), greater perceived accessibility (OR 2.40, 95 percent CI 1.66–3.47, p < 0.001), stronger conviction that AI proficiency will matter for their future careers (OR 1.77, 95 percent CI 1.23–2.55, p = 0.002) and, strikingly, stronger feelings that chatbot use is ethically inappropriate (OR 1.86, 95 percent CI 1.24–2.80, p = 0.003). Because these are unadjusted odds ratios, each figure reads as a simple multiplier: a clinical student carried roughly two and a half times the odds of rating accessibility higher than a pre-clinical peer. The overall pattern implies that proximity to real patients sharpens both sides of the ledger at once — deepening appreciation of what chatbots can do for a harried learner while simultaneously heightening awareness of where such tools do not belong.

That simultaneous elevation of enthusiasm and ethical alarm is the study’s subtlest finding. Clinical students were more likely than their juniors to insist that AI skills will be professionally indispensable — and more likely at the same time to call chatbot use ethically inappropriate. The two judgments are not contradictory; they are two faces of bedside realism. Senior students have seen enough of actual clinical decision-making to recognise where a language model genuinely accelerates learning, and where it intrudes on territory that belongs to supervised human judgement. Their wariness may also reflect a growing instinct for the boundaries that protect patient confidentiality, professional accountability and the integrity of medical advice. In effect, the students standing closest to medicine’s front line are sketching, unprompted, the very boundary map that professional bodies around the world are still struggling to draw.

Gender produced a second, unexpected fault line. Men were a minority of the sample — 65.8 percent of respondents were women — yet male students reported significantly greater knowledge of AI chatbots (OR 2.26, 95 percent CI 1.53–3.36, p < 0.001), more frequent use (OR 1.54, 95 percent CI 1.00–2.37, p = 0.049), greater perceived accessibility (OR 1.62, 95 percent CI 1.10–2.38, p = 0.014) and stronger feelings that such use is ethically inappropriate (OR 1.91, 95 percent CI 1.25–2.93, p = 0.003). The pairing is intriguing: male students were simultaneously more immersed in the technology and more critical of it, a combination that echoes broader findings in digital-health research where heavier exposure tends to sharpen, rather than dull, awareness of a tool’s limits. Whether the pattern reflects differing exposure to computing culture, divergent confidence in self-assessed knowledge or simply different response styles in surveys, the data cannot say — but it flags a gendered gap in technology adoption that medical educators would be unwise to ignore.

Perhaps the most quietly consequential number in the study is 58: the percentage of students who first learned about AI chatbots from their peers. Formal channels apparently trailed far behind peer word-of-mouth, meaning that the most transformative educational technology of the decade spread through Sri Lanka’s medical schools largely beneath the radar of the faculty charged with overseeing it. That informal diffusion route carries a double edge. On one side, it demonstrates genuine organic demand and the swift social contagion that viral tools enjoy among digitally native students. On the other, it means the norms governing use — when a chatbot is a legitimate study aid rather than a shortcut that hollows out learning, how rigorously an answer should be verified, what counts as academic honesty — were set by students for students, with no curricular guardrails, no ethical framing and no quality control. With assessment formats from multiple-choice questions to structured essay questions in the mix, that unregulated usage touches precisely the ground where medical faculties have the most to lose.

The authors’ prescription is unambiguous. AI chatbots are already woven into Sri Lankan medical training and are generally perceived favourably, they conclude, but that integration demands structured AI literacy education, explicit ethical guidance and faculty-led curricular integration — neither prohibition nor indifference. Translated into daily practice, that agenda means teaching students how large language models actually work and where they predictably fail; training them to verify machine-generated content against primary sources before it touches their notes, let alone their future patients; defining institutional policy on permissible use across coursework and examination preparation; and folding AI competencies into the formal curriculum instead of leaving them to peer folklore. The stakes stretch far beyond one island. Sri Lanka’s findings give the global medical-education community a rare window into how AI adoption unfolds in a health system outside the wealthy world, where student demand can run far ahead of institutional capacity to govern it. And the study’s closing message to educators everywhere lands with blunt force: your students are already using these tools. The only genuinely open question is whether you will teach them how.