Students are ready. The curriculum isn't.
New 2026 evidence shows four in five medical students want to learn about AI, yet most schools still offer little or no formal teaching and far fewer students feel able to use it in practice.
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Almost everything written about artificial intelligence in medical education is written from the front of the room. We argue about what to add to the curriculum, how to redesign assessment, and which competencies a graduate will need. The people sitting in the lecture theatre, the students who will actually inherit all of this, are too often treated as the objects of the debate rather than participants in it. So this week I want to turn the lens around and ask what the learners themselves think, how that thinking forms, and whether we can measure it well enough to act on.
I have selected four papers from 2026, and across the four the message is consistent: student appetite for AI is high and now well documented, but it runs well ahead of both student confidence and institutional provision. The interesting questions have moved on from whether students are interested to why their attitudes form the way they do, and what they are actually being offered in return.
Key points:
Student enthusiasm for AI is now backed by large-scale evidence: across 26 studies and almost 21,000 students, roughly four in five hold positive attitudes toward learning about AI.
That enthusiasm hides an “optimism-competence gap”, with far more students believing AI training is necessary than feeling able to use AI in practice.
How acceptance forms is partly cultural, as peer and social expectations shape uptake differently in different countries even when the underlying psychology is similar.
We are getting better at measuring readiness, with a validated multi-factor scale that captures intention to learn AI rather than just general approval.
Provision lags badly behind appetite: a whole-country audit found most medical schools offer no AI teaching at all, and what exists is mostly elective and unevenly distributed.
Mastour et al. provide the headline numbers in a systematic review and meta-analysis in BMC Medical Education that pools 26 studies and 20,963 medical students1. The top-line finding is reassuring for anyone pushing for reform: 78% of students held positive attitudes toward integrating AI into the curriculum, and that support was consistent across geographic regions. The more important finding is the one they name the “optimism-competence gap”. While 83.1% agreed that AI training is necessary, only 36.4% felt confident actually applying AI in clinical practice. Enthusiasm, in other words, is not the bottleneck.
The authors are candid about the limits of their synthesis, reporting extreme statistical heterogeneity (an I² of 98.5%), so the pooled percentages are best read as a broad signal rather than a precise figure. They also surface the practical barriers that keep the gap open:
curricular overcrowding (in 68% of the studies that discussed it)
a lack of faculty expertise (52%)
ethical concerns (41%).
Their conclusion, which I think is the right one, is that the task is no longer to persuade students but to build a standardised, competency-based AI curriculum that turns willingness into capability.
Xu et al. ask a subtler question in BMC Medical Education: not whether students accept AI, but through what psychological route, and whether that route travels across cultures2. They surveyed 1,232 medical students in China (681) and Pakistan (551) and used structural equation modelling to test how subjective norms, meaning the sense of what peers and respected others expect, feed into acceptance. Their model proposed that perceived usefulness and then attitude carry that social pressure through to acceptance, and that sequential path held in both countries.
What differed was the direct effect of social norms: in the Chinese sample subjective norms pushed acceptance directly (β = 0.231), while in the Pakistani sample the same direct effect was not significant (β = 0.113). The measurement model was invariant across the two groups, so this looks like a real cultural difference rather than a measurement artefact. The practical implication is that “students want AI” is too blunt a basis for strategy.
In some settings, signalling that AI use is expected and endorsed by faculty and peers will move the needle; in others, the case has to be made more directly on usefulness. Engagement strategies, in short, do not transplant cleanly from one system to another. I personally wonder how this finding intersects with differing cultural understandings of academic integrity.
Taghiparast et al. take on the unglamorous but essential job of measurement in Medical Education Online, adapting and validating a Persian version of the Artificial Intelligence Learning Intention Scale across 800 medical sciences students at one Iranian university3. If we are going to track readiness and judge whether our interventions work, we need instruments that measure the right thing, and this one holds up well.
Factor analysis supported a coherent four-part structure covering epistemic capacity, psychological attitudes, facilitating environments, and behavioural outcomes, with reliability statistics that were consistently strong (Cronbach’s alpha from 0.854 to 0.904) and an acceptable model fit (CFI of 0.932). Importantly, the scale behaved the same way for men and women, which matters if it is to be used to compare groups. This is a single-institution validation and the authors are appropriately modest about it, but the value is in giving educators a standardised way to capture students’ intention to engage with AI, not merely their vague approval of it. That distinction, intention to learn versus general positivity, is exactly what the Mastour gap suggests we should be watching.
Enériz Janeiro et al. then deliver the uncomfortable counterpoint in JMIR Medical Education: a census of what is actually on offer4. They examined every one of the 52 Spanish universities awarding an official medical degree, reviewing published curricula for the 2025-2026 year with two independent reviewers and external validation.
The result is sobering against the backdrop of all that student appetite. Only 16 universities (30.8%) had incorporated AI in any form, and just 10 (19.2%) offered a course in which AI was the primary subject. Public and private institutions were almost identical (around 19% each), so this is not a resourcing story in the obvious sense. Where AI did appear it was lightweight and optional, averaging 1.17% of the 360-credit degree and mostly elective, with only the University of Jaén mandating a course with AI content. The regional picture was the starkest part: Andalusia had a specific AI course in 5 of its 9 universities (55.6%), while 10 of Spain’s autonomous communities offered nothing at all.
The authors frame their taxonomy as a reusable tool for monitoring over time, which is welcome, because the headline is that national provision is patchy, shallow, and geographically unequal.
Put the learner’s view next to the institutional reality and the shape of the problem is clear. Students are ready, in numbers we can now quantify, and we are getting better at measuring just how ready and why. What is missing is not motivation but a coherent, required, and evenly distributed response from the schools that teach them. The optimism-competence gap that Mastour and colleagues describe is not really a gap in students at all; it is the space left by a curriculum that has not yet been built. The encouraging news is that none of this asks us to manufacture enthusiasm. It asks us to meet it, with structured teaching, culturally aware engagement, and honest measurement of whether confidence is catching up with appetite.
Mastour, H., Mirzaei, S., Sohrabi, S. et al. Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis. BMC Med Educ (2026). https://doi.org/10.1186/s12909-026-09630-9
Xu, X., Li, X., Alghamdi, A.A. et al. Cross-cultural determinants of AI acceptance in medical education among medical students in China and Pakistan. BMC Med Educ (2026). https://doi.org/10.1186/s12909-026-09675-w
Taghiparast, A. H., Mahmoudian, A., Ghaemi-Amiri, M., & Ghaffari, F. (2026). Cross-cultural adaptation and psychometric evaluation of the artificial intelligence learning intention scale (AILIS) among medical sciences students: a methodological study. Medical Education Online, 31(1). https://doi.org/10.1080/10872981.2026.2690326
Enériz Janeiro A, Pitombeira Pereira K,Mayol J, Crespo J, Carballo F, B Cabello J, Ramos-Casals M, Pérez Corbacho B,Turnes J. AI Integration in Spanish Undergraduate Medical Education: National Cross-Sectional Study. JMIR Med Educ 2026;12:e88511 doi: 10.2196/88511