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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Med Educ . 2026 Mar 10;26:630. doi: 10.1186/s12909-026-08931-3 Search in PMC Search in PubMed View in NLM Catalog Add to search Exploring health care learners’ perceptions of AI integration in the curriculum: a survey tool and findings Linda Chang Linda Chang 1 University of Illinois Chicago Rockford, Rockford, USA Find articles by Linda Chang 1, ✉ , Maura Polansky Maura Polansky 2 University of Illinois Chicago, Chicago, Illinois USA Find articles by Maura Polansky 2 , Darvin Yi Darvin Yi 2 University of Illinois Chicago, Chicago, Illinois USA Find articles by Darvin Yi 2 , Martin MacDowell Martin MacDowell 1 University of Illinois Chicago Rockford, Rockford, USA Find articles by Martin MacDowell 1 , Radhika Sreedhar Radhika Sreedhar 2 University of Illinois Chicago, Chicago, Illinois USA Find articles by Radhika Sreedhar 2 , Dawn Mosher Dawn Mosher 3 Saint Anthony College of Nursing- Rockford, Rockford, Illinois USA Find articles by Dawn Mosher 3 , Alan Schwartz Alan Schwartz 2 University of Illinois Chicago, Chicago, Illinois USA Find articles by Alan Schwartz 2 Author information Article notes Copyright and License information 1 University of Illinois Chicago Rockford, Rockford, USA 2 University of Illinois Chicago, Chicago, Illinois USA 3 Saint Anthony College of Nursing- Rockford, Rockford, Illinois USA ✉ Corresponding author. Received 2024 Dec 26; Accepted 2026 Feb 25; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13088441 PMID: 41808119 Abstract Background Few studies have assessed students in different healthcare disciplines’ perceptions of the importance of learning standard competencies in AI technology. This study aimed to evaluate the learning needs, apprehensions/anxieties, and digital self-efficacy of students in medicine, nursing, and pharmacy regarding AI technology in healthcare. Methods This cross-sectional survey study included 521 students: 384 medical, 67 nursing, and 78 pharmacy students, with response rates of 32%, 61%, and 52% from the colleges of medicine, nursing, and pharmacy, respectively. Survey data were collected over a 3-month period using a self-reported questionnaire. Data analysis was conducted using SPSS, including descriptive statistics, ANOVA, and chi-square tests. Results Students from all three disciplines agreed on the importance of learning about AI in healthcare, with agreement rates for the six core AI skill domains ranging from 80 to 92%. The AI Learning Anxiety/Fears questionnaire revealed varying anxiety levels about AI technology learning across the disciplines. Over 20% of students across all disciplines agreed or strongly agreed that they experience anxiety on the AI Learning Anxiety/Fears questionnaire. The digital self-efficacy scale score was negatively correlated with AI learning anxiety ( r =-0.32, p = 0.01). Conclusion This study offers insights into students’ perspectives on learning about AI, which will inform the development of effective AI curricula. These findings highlight the need for standardized AI curricula that can address learning needs and reduce anxiety across the healthcare disciplines. Supplementary Information The online version contains supplementary material available at 10.1186/s12909-026-08931-3. Keywords: AI, Student perceptions, Anxiety, Core competencies, Digital self-efficacy score Introduction Artificial Intelligence (AI), the creation of machines that mimic human cognition and behavior, is rapidly transforming healthcare [ 1 ]. As of October 2025, the U.S. Food and Drug Administration (FDA) has approved over 1,250 AI-based medical algorithms [ 2 ]. Non-clinical AI tools—those not requiring FDA approval—are proliferating across healthcare systems. These include applications in population health, revenue management, hospital monitoring, and preventive care. Generative AI is increasingly embedded in electronic medical records, streamlining workflows through features such as automated documentation, task prioritization, measurement assistance, and informed consent facilitation [ 3 ]. As AI becomes integral to clinical practice, future healthcare providers must be equipped to understand its mechanisms, limitations, and implications. Although they need not possess programming expertise, they need to be able to collaborate effectively with computer scientists and informaticians to ensure safe and ethical implementation of AI tools. Current AI algorithms are often trained on narrowly defined datasets, making them vulnerable to issues like data drift and bias, which can compromise patient safety and health equity [ 4 ]. Learning about an AI tool parallels acquiring knowledge about a new medication or diagnostic test, evaluating its mechanism, effectiveness, indication for patient cohorts, and potential risks As AI continues to evolve, so too must the skillsets of healthcare professionals. Despite the growing presence of AI in healthcare, most health professions curricula lack standardized training in AI competencies [ 5 , 6 ]. This gap leaves future providers underprepared to critically assess, implement, or question AI tools in clinical practice. Moreover, the rapid pace of technological advancement, coupled with already demanding academic workloads, may exacerbate student anxiety and hinder motivation to engage with AI education. Understanding learners’ perceptions, anxieties, and digital self-efficacy is essential to designing effective, discipline-sensitive AI curricula that foster both competence and confidence. Existing studies on students’ perceptions of AI are predominantly qualitative and often limited to single-discipline cohorts [ 7 – 21 ]. Most studies have found that healthcare students believe they need instruction on AI in healthcare. Although these studies offer insights into students’ familiarity with AI tools, their views on AI and patient care, and concerns about expressed anxiety about privacy, ethics, and job displacement, there remains limited data that explore students’ perceptions of the importance of acquiring skills aligned with recently identified AI competencies [ 6 ]. This leaves a critical gap in understanding how learners value specific capabilities needed for future clinical practice. One emerging barrier to AI education is AI-related anxiety, which may stem from “technophobia”—a condition marked by irrational fear or heightened discomfort with digital technologies [ 21 ]. This anxiety can lead to avoidance behaviors and negative attitudes toward AI learning. Wang and colleagues have categorized AI anxiety into three subtypes: AI Learning Anxiety, Job Replacement Anxiety, and Sociotechnical Blindness Anxiety [ 22 ]. Of these, AI Learning Anxiety refers to fear or discomfort specifically related to learning to use or adapt to AI technologies. A recent study involving nursing students identified anxiety as a significant barrier to AI adoption, underscoring the need to address emotional and psychological factors in curriculum design [ 23 ]. Learners’ emotional readiness and digital self-efficacy are other key predictors of students’ engagement with modern technologies. Confidence in digital literacy, familiarity with workplace systems, and prior exposure to innovation all influence healthcare providers’ willingness to adopt AI [ 24 ]. Studies on digital health literacy have also demonstrated a positive correlation between digital competence and favorable attitudes toward AI, suggesting that strengthening digital skills may help mitigate apprehension and foster more constructive engagement [ 25 , 26 ]. Building on this body of research, the present study aimed to assess medical, nursing, and pharmacy students’ learning needs, AI-related apprehensions, and digital self-efficacy in the context of healthcare AI education. This approach provides insight into learners’ readiness and helps guide the development of supportive AI literacy in healthcare curriculum integration. Method Research design This cross-sectional survey study aimed to gain insights into students’ attitudes and perceptions regarding integrating AI learning into their healthcare curricula. Site and participants Eligible participants were students at the Colleges of Medicine and Pharmacy at the University of Illinois Chicago and the College of Nursing at St. Anthony School of Nursing. The rationale for including these three different disciplines lies in their representation as the largest cohorts of health professionals in the healthcare sector. Both preclinical and clinical learners were invited to participate, and invitations were distributed through the respective colleges’ emails bi-monthly for over three months. Exclusion criteria included medical or pharmacy residents and graduate-level nursing students, which are defined as participants with more than five years of current discipline training. The Qualtrics Survey System was used to administer the survey and collect data. Students could enter a drawing for one of the twenty gift cards, each worth $25, to encourage student participation. Survey design The instrument consisted of three sections: Section 1 assessed students’ perceptions of their current academic workload using a 5-point rating scale ranging from “much too little” (1) to “much too much” (5). This section also evaluated students’ views on the importance of integrating recommended AI competencies into their curriculum, based on the Expert Consensus on AI Competencies for Healthcare Learners [ 6 ] (Table 1 ). We have labeled these as AI skill domain in our survey questionnaire. Section 2 measured AI-related learning anxiety using an adapted version of the AI Learning Anxiety subscale from Wang and Wang’s validated instrument [ 22 ]. This section focuses on emotional responses that may influence students’ engagement with AI-based educational resources and informed potential support strategies for curriculum integration (Table 2 ). Section 3 assessed digital self-efficacy using items adapted from a validated digital literacy instrument [ 24 ]. This section evaluated students’ confidence in using digital technologies and their perceived ability to adapt to emerging digital tools within healthcare settings (Table 3 ). Table 1. AI skill domain questionnaires 1. Application of AI in Health care 2. Evidence to support the use of common AI-based tools 3. Integrate information to enhance patient care 4. Improve workflow in the health care settings 5. Positive impact of AI on health care equity 6. Negative impact of AI on health care delivery Open in a new tab Table 2. AI learning anxiety/fears 1. Learning to use AI techniques/products makes me anxious 2. Learning to understand all of the special functions associated with an AI technique/product makes me anxious 3. Learning how an AI technique/product works makes me anxious 4. Learning to use specific functions of an AI technique/products makes me anxious 5. Learning to interact with an AI technique/product makes me anxious Open in a new tab Table 3. Digital self-efficacy 1. I can find where to access the right information on the internet 2. I can find how to access the correct information on the internet 3. I can adapt to new technologies 4. I can know how digital tools can work 5. I can confirm the accuracy of the information which I have accessed on the internet 6. I can use different digital devices 7. I can decide when digital tools will work 8. I can use necessary digital technologies to solve the problem 9. I would like to learn new information about digital technologies 10. I can benefit from expert guidance on new technologies Open in a new tab The survey also collected demographic information including students’ discipline (medicine, nursing, or pharmacy), training year, age, gender, and prior undergraduate coursework in fields such as computer science, artificial intelligence, engineering, social sciences, or biological sciences. Data analysis We used descriptive statistics to summarize demographic data and students’ perspectives on the importance of learning the six core AI competencies. Then, we scored the subscales in each section (importance of skill integration for each skill, digital use self-efficacy, and AI anxiety) by averaging associated items. We compared scale means using analysis of variance. Also, we compared the proportion of respondents from each health profession who reported agreement (vs. neutrality or disagreement) on relevant individual items using χ 2 tests. We calculated the correlation between digital self-efficacy and AI anxiety scales. Responses with unanswered items were excluded from the analysis and are reported as “missing” data. Assuming that the smallest meaningful difference in the importance scale would be ½ a standard deviation, we estimated that 64 learners per profession would provide 80% power to detect differences across health discipline learners. Data analysis was conducted using SPSS 2020. Results Our survey response included 529 students, comprising 384 medical (32%), 67 nursing (61%), and 78 pharmacy students (52%) respectively. The participants were 59% female, with a mean age of 27 years old, and 68% preclinical students. Students reported undergraduate training in the following disciplines: 86% in biological sciences, 80% in humanities/social sciences, 30% in computer science, 16% in engineering, and 15% in Data science/AI learning (Table 4 ). Table 4. Participants’ characteristics ( N = 529) Medicine ( n = 384) Nursing ( n = 67) Pharmacy ( n = 78) p -value of ANOVA (among groups) Overall ( n = 529) Gender ( n , %) Men 170(44.3) 4(6.0) 24(30.8) N/A 198 Women 205(53.4)) 61(91.0) 51(65.4) N/A 317 Missing 9(2.3) 2(3.0) 3(3.8) N/A 14 Age (mean, SD) (median) 26.9 (6.3) 26 35.2 (8.5) 34 24.9 (3.9) 24 < 0.001 N/A Academic year (n, %) 1 208(54.1) 32(47.8) 31(39.8) N/A 271 2 62(16.1) 7(10.4) 21(27.0) N/A 90 3 46(12.0) 10(15.0) 17(21.8) N/A 73 4 51(13.2) 7(10.4) 6(7.70) N/A 64 Residents/DNP * 13(3.4) 8(12.0) 1(1.28) N/A 22 Missing * 4(1.0) 3(4.5) 2(2.6) N/A 9 Undergraduate training (n, %) Had data science/AI learning 61(15.9) 9(15.3) 5(6.4) 0.091 75 Had biological science training 351(92.1) 42(71.2) 65(83.3) 0.001 458 Had computer science training 127(33.2) 19(32.2) 11(14.1) 0.003 157 Had engineering training 74(19.4) 2(3.4) 9(11.5) 0.004 85 Had humanities/social science training 324(85) 47(79.7) 53(67.9) 0.002 424 Open in a new tab *Not included in the analysis There were demographic differences between the groups in the age, gender, and undergraduate training categories. The nursing students were, on average, significantly older than other discipline students – Anova test P-value at 0.001. Students perceived educational needs related to the six core AI skills in healthcare We measured students’ perspectives on their educational needs related to 6 AI skill domains identified by expert consensus for all healthcare disciplines (Table 5 ). Table 5. Means (+/-SD) for questions related to students perceived educational related to AI skill domains Skill domain Medicine ( n = 384) Nursing ( n = 67) Pharmacy ( n = 78) p -value of ANOVA (among groups) 1.Application of AI in Health Care 3.40 (1.1) 3.64 (1.1) 3.47 (1.0) 0.22 2. evidence to support the use of common AI-based tools 3.49 (1.1) 3.54 (1.1) 3.37 (1.1) 0.60 3. Integrate information to enhance patient care 3.56(1.0) 3.87(0.9) 3.65(1.1) 0.09 4. Improve workflow in the health care settings 3.61(1.0) 3.65(1.0) 3.83(1.0) 0.23 5. Positive impact of AI on health care equity 3.61(1.1) 3.66(1.1) 3.73(1.0) 0.63 6. Negative impact of AI on health care delivery 3.98 (1.0) 3.79(1.0) 3.85 (1.0) 0.23 Open in a new tab Overall, the percentages of students ‘moderately to strongly agreed’ on the importance of learning these domains are 80% for AI application, 82% for evidence, 85% for integration, 86% for workflow, 85% for positive impact, and 92% for negative effect. Overall, no significant differences were observed when comparing the means for questions related to perspectives on learning AI-based skill domains between disciplines across all survey fields. AI anxiety scale- (AI learning anxiety/fears component) The responses from each discipline were analyzed, showing that over 20% of students reported ‘somewhat agree’ to ‘strongly agree’ on the AI Learning Anxiety/Fears questionnaire (Table 6 ). Table 6. AI learning anxiety/fears Survey Questions Medicine ( n = 384) Nursing ( n = 67) Pharmacy ( n = 78) p -value of χ 2 (among groups) % of students who reported somewhat agree to strongly agree Items 1. Learning to use AI tech/products makes me anxious 37.3 27.1 21.0 0.002 2. Learning to understand special AI functions make me anxious 37.1 22.4 23.0 0.03 3. Learning how an AI tech/product works makes me anxious 28.5 21.0 20.1 0.07 4. Learning to use specific functions of an AI tech/products makes me anxious 27.0 19.4 16.7 0.43 5. Learning to interact with an AI tech/product makes me anxious 31.7 30.4 24.3 0.82 Open in a new tab Overall, there were no observed significant differences between the three disciplines in the AI anxiety scale means (Table 7 ). However, there were two questions where statistical test results differed on cross-tabulation analysis as shown in Table 6 . Agreement with “Learning to use AI tech/products makes me anxious” differed significantly among groups ( p = 0.002), with greater agreement among medical students than other disciplines. a chi-square test showed a statistically significant difference (p-value = 0.002). A similar result was found for “learning to understand special AI functions make me anxious” ( p = 0.027). Table 7. Mean (SD) for questions related to AI learning anxiety/fears Survey Questions Medicine ( n = 384) Nursing ( n = 67) Pharmacy ( n = 78) p -value of ANOVA (among groups) Summary of mean score (SD) for the AI anxiety scale (sum of questions 1 to 5) 13.05 (5.82) 12.30 (5.71) 12.32 (5.28) 0.43 Items 1. Learning to use AI tech/products makes me anxious 2.70 (1.3) 2.52 (1.2) 2.47 (1.1) 0.23 2. Learning to understand special AI functions make me anxious 2.76 (1.3) 2.48 (1.2) 2.62 (1.1) 0.18 3. Learning how an AI tech/product works makes me anxious 2.51 (1.2) 2.3 (1.1) 2.38 (1.1) 0.33 4. Learning to use specific functions of an AI tech/products makes me anxious 2.52 (1.2) 2.37 (1.2) 2.38 (1.1) 0.46 5. Learning to interact with an AI tech/product makes me anxious 2.58 (1.3) 2.60 (1.3) 2.50 (1.2) 0.86 Open in a new tab Perceptions of digital fluency scale – (digital self-efficacy component) The majority (78–92%) of participants reported they ‘somewhat or strongly agreed’ with self-efficacy statements (Table 8 ). The digital self-efficacy scale score was negatively correlated with AI learning anxiety ( r =-0.32, p = 0.01). However, no significant differences were observed between disciplines in digital self-efficacy item or scale means. Table 8. Means (+/-SD) for questions related to digital self-efficacy Overall % agreement Medicine ( n = 384) Nursing ( n = 67) Pharmacy ( n = 78) p -value of ANOVA (among groups) Mean summary score for the digital self-efficacy scale (sum of questions 1 to 10, range 5–50)(SD) 42.28(5.00) 42.26(5.12) 42.47(5.16) 0.955 Items 1.I can find where to access the right information on the internet 84 4.11(0.84) 4.01(0.94) 4.10(0.75) 0.833 2. I can find how to access the correct information on the internet 86 4.16 (0.83) 4.00(0.83) 4.15(0.64) 0.327 3. I can adapt to new technologies 93 4.39(0.66) 4.51(0.61) 4.40(0.67) 0.388 4. I can know how digital tools can work 87 4.18(0.73) 4.29(0.71) 4.24(0.67) 0.412 5. I can confirm the accuracy of the information which I have accessed on the internet 84 4.02(0.78) 4.01(0.71) 4.13(0.71) 0.491 6. I can use different digital devices 93 4.48(0.69) 4.45(0.59) 4.47(0.62) 0.923 7.I can decide when digital tools will work 78 3.98(0.90) 4.13(0.67) 3.96(0.80) 0.354 8.I can use necessary digital technologies to solve the problem 87 4.19(0.71) 4.16(0.66) 4.21(0.73) 0.934 9.I would like to learn new information about digital technologies 86 4.29(0.73) 4.33(0.74) 4.26(0.73) 0.870 10.I can benefit expert guidance on new technologies 92 4.44(0.66) 4.40(0.65) 4.40(0.65) 0.806 Open in a new tab Discussion Across the disciplines, students expressed strong support for AI education, particularly in core competency areas, indicating a readiness to engage with AI-related content. This aligns with findings from other studies [ 13 , 15 , 16 , 19 , 20 ], which report that healthcare students recognize the importance of learning about AI and believe it should be integrated into their curricula. Despite demographic similarities among the groups, nursing students tended to be older and further along in their training. This may influence their perspectives on technology adoption and learning needs. Kwak found that senior nursing students demonstrated significantly more positive attitudes toward AI and reported lower anxiety levels [ 27 ]. These outcomes were attributed to their increased exposure to AI technologies and accumulated experience during clinical training, which enhanced their familiarity and confidence in applying digital tools in healthcare settings. However, our study also revealed notable levels of anxiety surrounding AI learning, especially among medical students. While the source of this anxiety remains unclear, it may reflect apprehension about technological displacement or discomfort with computational concepts [ 15 , 17 , 28 ]. These findings highlight the need for targeted support strategies to ease the transition into AI learning. Faculty can address this by designing introductory modules that demystify AI, emphasize its clinical relevance, and embed commonly used AI tools directly into the disease or organ system content students are studying. This integration allows learners to see AI applications in real-time clinical scenarios, reinforcing relevance and comprehension. Digital self-efficacy emerged as a significant factor influencing students’ comfort with AI. High self-efficacy correlated with lower anxiety, suggesting that confidence in using digital tools may buffer against fears related to AI learning. This result is consistent with the findings of a recent review of thirty-two studies suggesting that students with stronger digital backgrounds were more confident and less fearful of AI technologies [ 29 ]. Limitations This study has several limitations. Although the questionnaire incorporated recently identified AI skills domains in healthcare, the sections addressing AI learning anxiety and digital self-efficacy were adapted from existing tools rather than used in their original form. As a result, these scores are not directly translatable to the original tool validation studies. In addition, the items in the survey were broad and may not directly explore the underlying causes of AI-related anxiety. Since the questions were adapted from an existing AI Learning Anxiety scale validated in another population, we only included the items that assessed learners’ general anxiety and comfort level with engaging in AI-related educational activities and so may not reflect all causes of anxiety related to AI technology. This cross-sectional study provides only a snapshot of learners’ perceptions about incorporating AI into their curriculum at a single point in time, limiting its ability to track changes in attitudes over the course of their education. There is the potential for reporting bias, as some students may have been reluctant to admit to feelings of AI-related anxiety. It also did not explore other important dimensions of AI-related anxiety, such as concerns about job displacement, sociotechnical impacts, or the complexities of AI configuration and implementation. Our study has a lower number of responses from clinical students, which may affect the generalizability of findings across training stages. The nursing cohort was different from the medicine and pharmacy groups, given the greater representation of female gender. Literature suggests that gender is a factor that can influence AI-related attitudes, and we did not account for this [ 27 ]. Our sample size within gender subgroups is too small to support reliable subgroup statistical analysis. Future studies with larger and more balanced samples will be needed to examine potential gender-related differences in AI perceptions. Future research should explore alternative engagement strategies to ensure broader representation. The study’s generalizability is limited, as data was collected from two institutions potentially reducing the diversity of perspectives across different educational settings. Future studies, including a broader range of institutions and students with varied levels of clinical exposure, are necessary to fully understand AI’s evolving perceptions in medical education. Conclusion Our multi-disciplinary study found that students from all three disciplines agreed on the importance of learning about AI in healthcare. The AI Learning Anxiety/Fears questionnaire revealed varying anxiety levels about AI technology learning across the disciplines. This study offers insights into students’ perspectives on learning about AI, which will inform the development of effective AI curricula. These findings highlight the need for standardized AI curricula that can address learning needs and reduce anxiety across healthcare disciplines. Supplementary information Supplementary Material 1. (23.6KB, docx) Acknowledgments Not applicable. Authors' contributions LC and AS designed, analyzed and interpreted the survey data. MP, DY, RS, and DM reviewed and analyzed the survey data. MM analyzed and review the survey data. All authors read and approved the final manuscript. Funding Project was funded by our local Community Health Agency grant. Data availability All data generated or analyzed during this study are included in this published article. Declarations Ethics approval and consent to participate Our project has received our institution’s IRB exemption approval. The University of Illinois College of Medicine at Rockford IRB has taken the following action on IRBNet: Project Title: [2118775-3] Assessing Health Care Learners? Perceived Importance of AI Integration in the Curriculum: A Survey Tool Principal Investigator: Linda Chang, PharmD, MPH. Submission Type: Amendment/Modification Date Submitted: November 6, 2023. Action: APPROVED Effective Date: November 7, 2023 Review Type: Exempt Review. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Faiyazuddin M, Rahman S, Anand G, et al. The impact of Artificial Intelligence on healthcare: a comprehensive review of advancements in diagnostics, treatment, and operational efficiency. Health Sci Rep. 2025;8:e70312. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. 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[ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Material 1. (23.6KB, docx) Data Availability Statement All data generated or analyzed during this study are included in this published article. 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