The gap the scale fills is more than academic. Nursing education shapes the workforce that delivers the majority of direct patient care worldwide, and as AI-driven decision support, predictive analytics, and automated documentation become embedded in clinical practice, graduates must arrive fluent in these systems. That fluency depends on educators who can model, critique, and teach AI-related skills. Without a validated measure of faculty competency, curriculum committees have been designing AI integration strategies essentially blind, unable to benchmark where their educators stand or evaluate whether professional development programs actually work. The AI-CSNE was built to give institutions that benchmark, and its development followed the kind of methodological discipline that measurement scientists insist on but that few new instruments actually receive.

The researchers grounded the scale in the UNESCO Artificial Intelligence Competency Framework for Teachers, an international blueprint that outlines what educators across disciplines need to know about AI, supplemented by a review of the relevant literature. Content validity was established through expert evaluation and pilot testing before the instrument faced its real trial: a large cross-sectional survey of 470 nurse educators drawn from multiple nursing faculties in Egypt, conducted between mid-October 2025 and February 2026. Ethical approval came from the Scientific Research Ethics Committee of Tanta University, and participation was voluntary, anonymous, and electronically consented, with no personal identifiers collected at any point.

What makes the study methodologically interesting is how the authors handled validation. Rather than analyzing all 470 responses in a single pass, they randomly split the sample into two independent subsamples of 235 educators each. The first half was used for exploratory factor analysis, a statistical technique that lets the underlying structure of a questionnaire emerge from the data rather than being imposed by the researchers. The second, untouched half was reserved for confirmatory factor analysis, which tests whether the structure discovered in the first sample holds up in fresh data. This two-step design is considered the gold standard in scale development because it guards against overfitting, the statistical equivalent of memorizing answers rather than learning the subject.

The exploratory analysis revealed a clean three-factor structure. The first domain, Foundational AI Knowledge and Instructional Application, captures the technical and pedagogical core: understanding what AI is, how it works, and how to weave it into teaching. The second, Ethical and Human-Centered AI Use, addresses the values dimension, including responsible deployment, awareness of bias, and keeping human judgment at the center of care education. The third, AI-Supported Professional Growth, reflects educators’ use of AI for their own continuing development. Factor loadings, which indicate how strongly each item relates to its domain, ranged from 0.661 to 0.822, and together the three factors explained 56.10 percent of the total variance in responses, a respectable share for a multidimensional instrument.

Reliability, the consistency with which the scale measures what it measures, was assessed using Cronbach’s alpha. The Foundational AI Knowledge and Instructional Application domain scored 0.842, and Ethical and Human-Centered AI Use scored 0.810, both comfortably within the range conventionally regarded as satisfactory. The AI-Supported Professional Growth domain came in at 0.697, slightly below the traditional 0.70 threshold but close enough that the authors judged it acceptable for a newly developed scale, with comparable estimates appearing in the confirmatory subsample. These figures suggest the scale produces stable, internally coherent measurements across its domains, though the third subscale may benefit from refinement in future iterations.

The confirmatory factor analysis delivered the study’s most striking numbers. The three-factor model achieved a Comparative Fit Index of 0.989 and a Tucker-Lewis Index of 0.986, both far above the 0.95 benchmark typically considered evidence of excellent fit. The Root Mean Square Error of Approximation was 0.023 and the Standardized Root Mean Square Residual was 0.046, both well under the cutoffs that signal good model fit. In plain terms, the structure discovered in the first half of the sample reproduced almost perfectly in the second half, giving strong initial evidence that the AI-CSNE genuinely measures three coherent dimensions of perceived AI competency rather than statistical noise. Within the confirmatory sample, the Foundational Knowledge domain showed the highest standardized loadings, between 0.626 and 0.785, while the Ethics domain loaded somewhat lower, from 0.570 to 0.734, still within acceptable bounds.

One finding carries particular weight for anyone planning to use the instrument: the correlations between the three domains were near zero. This means the subscales are empirically distinct, tapping genuinely separate facets of competency rather than slightly different expressions of a single underlying ability. The practical consequence is that scores should be interpreted domain by domain rather than summed into one overall AI competency score. An educator might be technically knowledgeable yet ethically cautious, or professionally curious yet lacking instructional skills, and the scale’s structure is designed to expose exactly those profiles. The authors also assessed convergent, criterion, and known-groups validity, finding no significant differences across demographic subgroups, which they interpret cautiously as a sign that further validation work remains necessary.

Perhaps the most important caveat concerns what the scale does not measure. The AI-CSNE captures perceived competency, what educators believe they can do, not objectively demonstrated skill. Self-perception and actual ability can diverge, sometimes dramatically, in any domain of expertise. The authors are explicit that the instrument is therefore best suited for exploratory group-level needs assessment, research comparisons, program evaluation, and self-reflective faculty development, rather than for individual appraisal, certification, or high-stakes evaluation of any kind. Used appropriately, however, it offers nursing schools a practical starting point: a way to map where their faculty stand on AI knowledge, ethics, and professional growth, and to design training that targets real weaknesses. As AI continues its advance into clinical education, instruments like this one, built on transparent psychometric foundations and tested against independent data, will be essential for ensuring that the humans teaching the caregivers are not left behind by the machines now entering the classroom.

Subject of Research: Development and psychometric validation of a three-domain scale measuring perceived artificial intelligence competency among nurse educators

Article Title: Development and structural validation of the Artificial Intelligence Competency Scale for Nurse Educators (AI-CSNE): a three-domain measure of perceived AI competency

Article References: Mohamed, M. G., Mohamud, Z. A., & Sorour, M. S. (2026). Development and structural validation of the Artificial Intelligence Competency Scale for Nurse Educators (AI-CSNE): a three-domain measure of perceived AI competency. BMC Nursing. https://doi.org/10.1186/s12912-026-05453-7

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05453-7

Keywords: artificial intelligence, nurse educators, psychometric validation, scale development, nursing education, factor analysis, competency assessment, UNESCO AI Competency Framework, confirmatory factor analysis, faculty development, health professions education, self-perceived competency

The study, led by Laudine Ackermans and Anne Spinewine of UCLouvain together with a broad consortium of researchers across Belgian universities and university hospitals, set out to evaluate the extent to which current education equips future healthcare professionals to deprescribe. Between April and June 2024, the team administered a cross-sectional online survey to undergraduate and postgraduate students in medicine, pharmacy and nursing enrolled at Belgian universities and nursing schools. The response was remarkable in scale: 1,777 students completed the questionnaire, including 509 medical students, 792 pharmacy students and 476 nursing students. This large, multi-disciplinary sample allowed the investigators to compare how different professional programs address deprescribing and to identify the factors most strongly associated with students feeling competent to carry it out.

The survey instrument was carefully grounded in established behavioral and educational science. Self-perceived competencies were assessed through seven items, while knowledge and skills were measured with nineteen items drawn from Farrell’s framework for deprescribing education. Attitudes were captured using twenty-five items from a questionnaire based on the Theoretical Domains Framework, a widely used model for understanding the psychological determinants of professional behavior, originally developed by Michie and colleagues and later validated by Cane and coworkers. The researchers analyzed the data using descriptive statistics and linear regression models, in which composite competency scores served as outcomes and curriculum level, clinical experience and exposure to deprescribing education acted as predictors. Open-ended responses were also thematically coded, giving students a voice in shaping the conclusions.

One of the most consequential findings concerns the sheer asymmetry of educational exposure across disciplines. Deprescribing featured commonly within the curricula of medicine and pharmacy programs, but it was rare in nursing education. This gap matters because nurses are frequently the professionals closest to patients, monitoring their day-to-day responses to medications and often best positioned to notice when a drug is causing harm or no longer serving a purpose. Previous work, including a 2026 scoping review in the International Journal of Nursing Studies on nurses’ role in deprescribing for older adults, has argued that nurses are underutilized in this domain. The Belgian survey now provides quantitative confirmation that the preparation of nursing students in deprescribing lags substantially behind that of their medical and pharmacy peers.

Among medicine and pharmacy students, the regression analyses revealed a clear and statistically robust pattern: greater self-reported exposure to deprescribing within the curriculum and a more advanced academic level were both significantly associated with higher self-perceived competency scores, with p values below 0.001. In other words, students who had encountered deprescribing more often, and those further along in their training, felt markedly more capable of engaging in it. While self-perceived competence is not the same as demonstrated skill, self-efficacy is a well-established predictor of whether professionals actually adopt new practices, making this finding a meaningful signal about which educational ingredients drive readiness. The dose-response relationship between exposure and confidence suggests that simply embedding deprescribing more deeply and repeatedly into curricula could pay dividends.

Perhaps the most technically revealing insight concerns where in the deprescribing process students feel confident and where they falter. Across all three disciplines, students reported higher confidence in the early steps of the deprescribing process, such as recognizing when a medication might be a candidate for withdrawal, but markedly lower confidence in assessing the deprescribing potential of individual medications and in implementing and monitoring actual deprescribing plans. This is a critical distinction. The early steps rely on general awareness and attitude, while the later steps demand specific pharmacological knowledge, familiarity with tapering schedules, and the clinical judgment to manage withdrawal effects and relapse risk. Evidence-based deprescribing frameworks, such as the patient-centered process described by Reeve and colleagues in the British Journal of Clinical Pharmacology and the GRADE-based methodology of Farrell and coworkers, emphasize exactly these later stages. The survey suggests that current education tends to cover the concept of deprescribing well but the operational mechanics of it poorly.

The attitudinal data offer a genuinely encouraging counterpoint. Students across all groups expressed generally positive attitudes, with strong intentions to deprescribe in their future practice, clear perceptions of its benefits, and a sense that deprescribing aligns with their emerging professional identity. This is significant because attitude and identity are powerful levers for behavior change; a workforce that enters practice already viewing deprescribing as part of its professional role is far more likely to sustain the behavior than one that must be persuaded later. At the same time, the students identified barriers that practicing clinicians will find intimately familiar: patient reluctance to stop medications, competing clinical priorities, and time constraints. These barriers echo those documented in systematic reviews of prescriber behavior and in surveys of European physicians regarding benzodiazepine deprescribing, underscoring that education alone, however well designed, will need to be complemented by system-level support if deprescribing is to become routine.

The students themselves did not stop at identifying problems; they offered a prescription of their own. In the open-ended responses, they called for more practical and interprofessional education on deprescribing, introduced earlier in the curriculum and delivered more broadly across disciplines. This aligns closely with a proposed curricular framework for interprofessional deprescribing education published by Farrell and colleagues in Medical Science Education in 2023, and with earlier work by Zimmerman and coworkers showing that trainees in medicine, pharmacy and nursing perceive gaps in their preparation and want clearer guidance on each other’s roles in the deprescribing process. Interprofessional education is particularly relevant because successful deprescribing in real-world settings is almost never a solo act; it typically requires a physician willing to revise a prescription, a pharmacist able to design a tapering plan, and a nurse monitoring the patient’s response, all coordinated around the patient’s own goals and preferences.

The Belgian context makes these findings especially timely. Belgium faces the same demographic pressures as much of Europe, with a growing population of older adults exposed to potentially inappropriate prescribing, a problem documented in national studies of nursing homes and community-dwelling patients with polypharmacy. National initiatives, including a reimbursed program to support withdrawal from benzodiazepines and related Z-drugs, have signaled policy-level commitment to tackling overmedication, and international deprescribing networks have been working to translate guidelines into practice. Yet interrupted time-series analyses have shown that the publication of international deprescribing guidelines alone does not automatically change prescribing behavior in nursing homes. The new survey points to a plausible missing link: if the professionals of tomorrow are not systematically trained in the later, operational stages of deprescribing, guidelines will continue to underperform in the clinic.

The authors are careful to acknowledge the limits of self-reported data. Self-perceived competency can be inflated, a phenomenon famously described in the psychological literature on the Dunning-Kruger effect, and the survey’s cross-sectional design captures a snapshot rather than a trajectory. Nevertheless, the scale of the sample, the grounding of the instruments in validated frameworks, and the consistency of the patterns across disciplines lend the findings considerable weight. The practical implications are clear: nursing curricula urgently need to incorporate deprescribing; all three disciplines need more emphasis on assessing the deprescribing potential of specific drugs and on implementing tapering plans; and education should be practical, interprofessional and introduced early. If educators act on these findings, the generation of clinicians now in training may be the first to treat deprescribing not as an afterthought to prescribing, but as an equally core clinical skill, one that could meaningfully reduce the hidden burden of overmedication that millions of older patients carry every day.

Subject of Research: A national survey of self-perceived deprescribing competencies, knowledge, skills and attitudes among healthcare professional students in Belgium.

Article Title: Self-Perceived Competencies, Knowledge, Skills, and Attitudes of Healthcare Professional Students in Deprescribing: A National Survey

Article References: Ackermans, L., Van Durme, T., de Saint-Hubert, M., Guisset, S., Van Leeuwen, E., Coteur, K., Tommelein, E., Philippe, G., Dilles, T., Verschuren, S., De Vriese, C., Patris, S., Siriez, R., Vanpee, D., & Spinewine, A. (2026). Self-Perceived Competencies, Knowledge, Skills, and Attitudes of Healthcare Professional Students in Deprescribing: A National Survey. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10724-9

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10724-9

Keywords: deprescribing, polypharmacy, health professional education, medical students, pharmacy students, nursing students, self-perceived competency, interprofessional education, medication safety, older adults, attitudes, Belgium