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Cited by 30Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationArtificial Intelligence in Education

A Training Needs Analysis for AI and Generative AI in Medical Education: Perspectives of Faculty and Students

Open access
McCoy, Lise, Ganesan, Natarajan, Rajagopalan, Viswanathan, McKell, Douglas, Niño, Diego F., Swaim, Mary Claire·2025·Journal of Medical Education and Curricular Development
DOI 10.1177/23821205251339226PMC PMC12078978
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Abstract

Introduction: The growing presence of artificial intelligence (AI) in health professions has created a need to investigate its potential benefits and challenges in medical education. This article presents findings from an AI learner training needs analysis survey at a U.S. medical school. It compares faculty and student experiences and perspectives on using generative AI (GAI) and other AI tools for undergraduate medical education, focusing on their respective knowledge and learning preferences. Methods: Faculty and students were surveyed using an online cross-sectional survey design to assess their GAI experience, AI patterns of use, adoption readiness, and training preferences. Surveys contained 14 to 15 multiple-choice items, with 8 items including a write-in option. A total of 68 faculty and 506 students responded to the survey, with a 50% response rate for faculty and 30% for students. Statistical tests were used to determine whether students and faculty differed significantly in their GAI experience. Results: < .001) but not significantly more experienced with GAI tools. There were no significant differences in frequency of use. Both groups considered AI tools and technology useful for personal, academic, research, and clinical applications. More than half of both groups were using AI for academic tasks. Both groups expressed concerns about the reliability of AI output, with faculty showing a much greater level of concern. Both groups identified several training formats as beneficial, with faculty preferring formal training (either online or in-person), followed by peer tutorials and self-study. On the other hand, students showed slightly greater interest in self-study than other formats. Conclusion: Our findings will inform the design of two parallel structured AI training programs, focusing on faculty and student priorities, including hands-on skills practice, and emphasizing AI's ethical use, reliability, and limitations.

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AI snapshot

Written from the paper’s full text.

TL;DR

This training needs analysis surveyed faculty and students at one U.S. osteopathic medical school about generative AI experience, use, readiness, and training preferences. Students were significantly more familiar with GAI than faculty, but the groups did not differ significantly in prior GAI tool use or frequency of use, and both reported reliability concerns.

Key findings

  • Students were significantly more familiar with GAI chatbots than faculty (P < .001): 76% of students vs 57% of faculty agreed or strongly agreed they were familiar.
  • Prior GAI tool use was similar: faculty 62% and students 59% agreed or strongly agreed they had used a GAI tool, with no significant difference; most respondents reported never or rarely using chatbots/AI tools (faculty 60%; students 77%).
  • More than half of both groups had used AI for academic tasks (faculty 56%; students 77%), and both groups expressed concerns about AI output reliability, with faculty showing greater concern in all categories.
  • Training preferences differed: faculty rated formal online training highest (59%), while students rated self-study highest (29%); faculty’s top resource priorities were hands-on AI skills training (61%), academic integrity/use guidelines (48%), and a protected faculty development day (46%).

Methods

  • Online cross-sectional survey at NYITCOM’s Old Westbury, NY, and Jonesboro, AR, campuses; all 1831 potential participants were included (1696 medical students across MS1–MS4 and 135 faculty, including 2 administrative staff).
  • A total of 68 faculty and 506 students responded, yielding 50% and 30% response rates, respectively; data collection occurred February–April 2024.
  • Two survey forms were used (faculty: 15 items; students: 14 items), with 8 items including a write-in option; domains covered GAI experience, AI use patterns, adoption readiness, and training preferences. Analysis used t-tests for Q5, Q6, Q8, and Q10, plus thematic analysis of qualitative responses.

Limitations

  • Stated: conducted at a single medical school, so results are not easily generalizable; most student responses (90%) came from MS1/MS2, with fewer MS3/MS4 because they were off-campus on clinical rotations; GAI tools evolved during the 3-month data collection; no a priori power analysis was performed; usage data are a starting layer, not a definitive assessment of competence or readiness for clinical use.
  • Stated: for several survey items, tables were created for only faculty or only students; the study could have captured more faculty conversations about AI for teaching and research.
  • Evident from the text: the design is voluntary, self-report, and cross-sectional, with very low MS3 (5%) and MS4 (7%) response rates and 0 responses from the MS1 off-cycle enrichment cohort, limiting subgroup comparisons.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1177/23821205251339226

Cites

39 of 39 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

19 of 29 cited sources checked · 12 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Literature searches located 7 survey research studies focusing on student or faculty AI perspectives.

    “Literature searches located 7 survey research studies focusing on student or faculty AI perspectives14–20; only three published AI training needs assessment studies were conducted in a U.S. medical school.15,19,21”
    Quote in the paperSubstantial claim6 of 7 sources checked
    • cites [14] Civaner M Uncu Y Bulut F, et al. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ. 2022; 22(09 November): 772. doi: 10.1186/s12909-022-03852-3 36352431 PMC9646274 source
    • cites [15] Hosseini M Gao CA Liebovitz DM, et al. An exploratory survey about using ChatGPT in education, healthcare, and research. PLoS One. 2023; 18(10): 1‐ 39. doi: https://doi.org/10.1371%2Fjournal.pone.0292216 PMC10553335 37796786 source
    • cites [16] Sallam M Salim NA Barakat M, et al. Assessing health students’ attitudes and usage of ChatGPT in Jordan: validation study. JMIR Med Educ. 2023; 9: e48254. doi: 10.2196/48254 PMC10509747 37578934 source
    • cites [17] Weidener L Fischer M. Artificial intelligence in medicine: cross-sectional study among medical students on application, education, and ethical aspects. JMIR Med Educ. 2024; 10: e51247. doi: 10.2196/51247 PMC10799276 38180787 source
    • cites [18] Salih S. Perceptions of faculty and students about use of artificial intelligence in medical education: a qualitative study. Cureus. 2024; 16(4): e57605. doi: 10.7759/cureus.57605 PMC11069392 38707183 source

      The Abstract says the study examines faculty and students’ perspectives on AI. But Materials and methods identifies it as a qualitative case study using interviews and focus groups, not a survey study. This paper also cannot establish that the citing authors’ literature search found seven studies. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [19] Wood E Ange BL Miller DD. Are we ready to integrate artificial Intelligence literacy into medical school curriculum: students and faculty survey 2021. J Med Educ. 2021; 8(Jun 23): 1- 5. doi:. 10.1177/23821205211024078 PMC8239949 34250242 source
    • cites [20] Kimmerle J Timm J Festl-Wietek T Cress U Herrmann-Werner A. Medical students’ attitudes toward AI in medicine and their expectations for medical education. J Med Educ. 2023; 10(S3): 1- 3. doi: 10.1177/23821205231219346 PMC10704950 38075443 source
  • Premisecitation

    Only three published AI training needs assessment studies were conducted in a U.S. medical school.

    “only three published AI training needs assessment studies were conducted in a U.S. medical school.15,19,21”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites [15] Hosseini M Gao CA Liebovitz DM, et al. An exploratory survey about using ChatGPT in education, healthcare, and research. PLoS One. 2023; 18(10): 1‐ 39. doi: https://doi.org/10.1371%2Fjournal.pone.0292216 PMC10553335 37796786 source

      The Introduction and Methods describe an exploratory ChatGPT survey organized at Northwestern University that included medical trainees. But the Results report a broader academic audience, with only 14 medical trainees among 420 respondents; the paper assesses ChatGPT use and attitudes, not specifically AI training needs. This source identifies one survey but does not establish the total of three studies. (LLM, conf=0.83)

      Experimental — accuracy not yet measured
    • cites [19] Wood E Ange BL Miller DD. Are we ready to integrate artificial Intelligence literacy into medical school curriculum: students and faculty survey 2021. J Med Educ. 2021; 8(Jun 23): 1- 5. doi:. 10.1177/23821205211024078 PMC8239949 34250242 source
    • cites [21] Cervantes J Blake S Ramadoss T, et al. Decoding medical educators’ perceptions on generative artificial intelligence in medical education. J Investig Med. 2024; 72(7): 1- 12. doi: 10.1177/10815589241257215 38785310 source
  • Findingown data

    A greater percentage of faculty used AI for research than students (28% vs 12%).

    “but a greater percentage of faculty used AI for research than students (faculty 28%; students 12%).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the broader NYITCOM faculty and student population with respect to AI experience and training preferences.
  • Findingown data

    Approximately two-thirds of both faculty and students had used GAI tools (faculty 62%, students 59%).

    “Approximately two-thirds of both groups agreed or strongly agreed that they had used GAI tools (faculty 62% and students 59%).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the broader NYITCOM faculty and student population with respect to AI experience and training preferences.
  • Findingown data

    There was no significant difference between faculty and students in prior GAI tool use.

    “A 2-tailed t-test indicated that there was no significant difference between groups.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Likert-scale responses can be treated as interval/continuous data for t-tests.
    • The authors state this as an assumption: The data are normally distributed.
    • Rests on an assumption: The two survey forms (faculty and student) measure the same constructs comparably enough to compare group means.
  • Classified as probably not a substantial claim. 26
  • May combine two claims. 27
  • The quote only partly states this claim. 1

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    In 2023, GAI tools such as ChatGPT became widely accessible to the public and quickly gained popularity in higher education, medical education, and healthcare delivery.

    “In 2023, GAI tools such as ChatGPT suddenly became widely accessible to the public, quickly gaining popularity in higher education, medical education, and healthcare delivery.4-6”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites [4] Gershov S Braunaold D Spektor R, et al. Automating medical simulations. J Biomed Inform. 2023; 144(Aug): 10. doi: 10.1016/j.jbi.2023.104446 37467836 source
    • cites [5] Zawacki-Richter O Marin V Bond M, et al. Systematic review of research on artificial intelligence applications in higher education–where are the educators? Int J Educ. 2019; 16(39): 27. doi: 10.1186/s41239-019-0171-0 source

      The Introduction (page unavailable) says AI applications in education were rising and attracting attention, which supports only the broad higher-education trend. The paper discusses earlier AI applications, not the public availability or popularity of ChatGPT and other generative AI tools in 2023, or their popularity in medical education and healthcare delivery. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites [6] Khan R Jawaid M Khan AR, et al. ChatGPT - reshaping medical education and clinical management. Pak J Med Sci. 2023; 39(2): 605. doi: 10.12669/pjms.39.2.7653 36950398 PMC10025693 source

      The Background calls ChatGPT the most widely known chatbot discussed and describes potential uses in medical education; the Conclusion discusses its potential role in clinical management. The paper does not establish that generative AI tools became publicly accessible in 2023 or quickly gained popularity across higher education, medical education, and healthcare delivery. Its discussion of clinical use is largely prospective, not evidence of widespread adoption. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The reporting of this study conforms to the Defined Criteria to Report INnovations in Education.

    “The reporting of this study conforms to the Defined Criteria to Report INnovations in Education. 22”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [22] Blanco M Prunuske J DiCorcia M Learman LA Mutcheson B Huang GC. The doctrine guidelines: defined criteria to report innovations in education. Acad Med. 2022; 1(97): 689‐ 695. 35171122 source

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Premisecitation

    AI is a rapidly expanding field with profound implications for medical education, clinical practice, and scientific research.

    “AI is a rapidly expanding field with profound implications for medical education, clinical practice, and scientific research.1-3”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites [1] Pizzola I Aro R Duez P, et al. Integrating artificial intelligence into medical education: lessons learned from a Belgian initiative. JILR. 2023; 34(2): 43.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [2] Gordon M Daniel M Ajiboye A, et al. A scoping review of artificial intelligence in medical education: BEME guide no. 84. Med Teach. 2024; 46(4): 446‐ 470. doi: 10.1080/0142159X.2024.2314198 38423127 source
    • cites [3] Patino G Amiel J Brown M, et al. The promise and perils of artificial intelligence in health professions education practice and scholarship. Acad Med. 2024; 99(5): 5. doi: 10.1097/ACM.0000000000005636 38266214 source

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Premisecitation

    The adoption of GAI technologies is expected to significantly transform medical education, creating a call to action for the medical education community to acquire resources and implement plans to navigate these changes effectively.

    “The adoption of GAI technologies is expected to significantly transform medical education, creating a “call to action” for the medical education community to acquire resources and implement plans to navigate these changes effectively. 7”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [7] Lomis K Jeffries P Palatta A, et al. Artificial intelligence for health professions educators. NAM perspectives. 2021; 2021(Sep 8): 10‐ 31478. PMC8654471 34901780 source

      In “Next Steps: Answering the Call to Action” (page not provided), the paper explicitly calls for health professions educators to act now and plan how to incorporate AI into training. It discusses resource constraints, but the passages address AI broadly, not generative AI (GAI) specifically, and do not explicitly call for acquiring resources. (LLM, conf=0.87)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Premisecitation

    The first step in designing an effective training program is to assess the knowledge base, user experience, and learning preferences.

    “The first step in designing an effective training program is to assess the knowledge base, user experience, and learning preferences. 13”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [13] Raffing R Larsen S Krong L, et al. From targeted needs assessment to course ready for implementation—A model for curriculum development and the course results. Int J Environ Res Public Health. 2023; 20(2592): 11. doi: 10.3390/ijerph20032529 PMC9915190 36767895 source

      The Materials and Methods section says course development began by interviewing targeted learners about their needs, including their experience of writing a first scientific article. The paper also explored learners’ views of the proposed course, but it does not specify assessing their prior knowledge or learning preferences as the first step; its Introduction says the curriculum-development steps can be taken in different orders. The indexed copy provides no page numbers for these passages. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Faculty, often from Baby Boomer or Generation X cohorts, typically have less technology exposure than Generation Z students, who are more adept with digital technologies.

    “Faculty, often from Baby Boomer or Generation X cohorts, typically have less technology exposure than Generation Z students, who are more adept with digital technologies.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [35] Culpe-Roche A Hampton D Hensley A, et al. Generational differences in faculty and student comfort with technology use. SAGE Open Nurs. 2020; 6(July 15): 1- 6. doi: 10.1177/2377960820941394 PMC7774396 33415296 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Several publications corroborate the crucial nature of addressing concerns about AI ethics, critical thinking, privacy, and data integrity.

    “Several publications corroborate the crucial nature of addressing these concerns.”
    Quote in the paperNot a substantial claim3 of 4 sources checked
    • cites [8] Halaweh M. ChatGPT in education: strategies for responsible implementation. Contemp Educ Technol. 2023; 15(2): 11. source

      The Introduction identifies privacy, lack of critical thinking, bias, inaccuracies, and plagiarism as concerns and says the paper aims to address them through responsible use of ChatGPT. The Literature Review likewise focuses on authentic learning and academic honesty. These passages support the importance of addressing several named concerns, but our copy does not specifically address data integrity. (LLM, conf=0.83)

      Experimental — accuracy not yet measured
    • cites [21] Cervantes J Blake S Ramadoss T, et al. Decoding medical educators’ perceptions on generative artificial intelligence in medical education. J Investig Med. 2024; 72(7): 1- 12. doi: 10.1177/10815589241257215 38785310 source
    • cites [38] Alam FLM Zulkipli IN. Integrating AI in medical education: embracing ethical usage and critical understanding. Front Med. 2023; 10: 1279707. doi: 10.3389/fmed.2023.1279707 PMC10611520 37901398 source
    • cites [39] Charow R Jeyakumar T Younus S, et al. Artificial intelligence education programs for health care professionals: scoping review. JMIR Med Educ. 2021; 7(4): E31043. doi: 10.2196/31043 PMC8713099 34898458 source

  • Probably not a substantial claim: may not pass S3 (load-bearing). The quote only partly states this claim.

    Interpretationcitation

    The finding that 56% of faculty and 70% of students used AI for teaching/learning tasks is consistent with numerous studies highlighting various applications of AI in medical education.

    “This finding is consistent with numerous studies highlighting various applications of AI in medical education.”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites [2] Gordon M Daniel M Ajiboye A, et al. A scoping review of artificial intelligence in medical education: BEME guide no. 84. Med Teach. 2024; 46(4): 446‐ 470. doi: 10.1080/0142159X.2024.2314198 38423127 source
    • cites [3] Patino G Amiel J Brown M, et al. The promise and perils of artificial intelligence in health professions education practice and scholarship. Acad Med. 2024; 99(5): 5. doi: 10.1097/ACM.0000000000005636 38266214 source
    • cites [37] Hale J Alexander S Towner Wright S Gilliland K. Generative AI in undergraduate medical education: a rapid review. J Med Educ. 2024; 11: 15. doi: 10.1177/23821205241266697 source

  • May combine two claims.

    Findingown data

    Students were significantly more familiar with GAI chatbots than faculty (P < .001); 76% of students vs 57% of faculty agreed or strongly agreed they were familiar.

    “Students were significantly more familiar with GAI than faculty (P < .001) but not significantly more experienced with GAI tools.”
    • Also stated in: § Domain 1: Experience With GAI Tools · boosted
      “Student responses demonstrated greater familiarity with GAI tools than faculty (P < .001). Combining agree and strongly agree, 76% of students were familiar versus 57% of faculty.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Likert-scale responses can be treated as interval/continuous data for t-tests.
    • The authors state this as an assumption: The data are normally distributed.
    • Rests on an assumption: The two survey forms (faculty and student) measure the same constructs comparably enough to compare group means.

  • May combine two claims.

    Findingown data

    More students (42%) were comfortable using AI for teaching, learning, clinical, and research purposes than faculty (34%), but the difference was not statistically significant.

    “Combining the responses for “strongly agree” and “agree,” more students (42%) were comfortable using AI than faculty (34%). A 2-tailed t-test indicated that there was no significant difference between groups.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Likert-scale responses can be treated as interval/continuous data for t-tests.
    • The authors state this as an assumption: The data are normally distributed.
    • Rests on an assumption: The two survey forms (faculty and student) measure the same constructs comparably enough to compare group means.

  • May combine two claims.

    Findingown data

    Both faculty and students used AI for academic medical education (faculty 37%, students 24%).

    “Both groups used AI for academic medical education (faculty 37%; students 24%), but a greater percentage of faculty used AI for research than students (faculty 28%; students 12%).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the broader NYITCOM faculty and student population with respect to AI experience and training preferences.

  • May combine two claims.

    Findingown data

    Students reported AI as beneficial to the entire range of subjects, with pharmacology and biochemistry ranking highest (14% and 12%).

    “students reported AI as beneficial to the entire range of choices, with pharmacology and biochemistry ranking the highest, 14% and 12%, respectively.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the broader NYITCOM faculty and student population with respect to AI experience and training preferences.

  • May combine two claims.

    Findingown data

    Student write-in concerns about AI output focused on reliability, over-reliance, societal impacts, and too much work.

    “Student write-in concerns focused on GAI and were grouped into 4 categories: Reliability, Over-Reliance, Societal Impacts, and Too much work.’”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    56% of faculty and 70% of students had used AI for the specific teaching/learning tasks listed.

    “When asked how they used AI for teaching or learning, 56% of faculty and 70% of students had used AI for the specific teaching/learning tasks listed”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey respondents are representative of the medical school's faculty and students.
    • Rests on an assumption: The survey instrument validly measures AI experience, usage patterns, attitudes, and training preferences.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Most student responses (90%) were from MS1 and MS2.

    “Most student responses (90%) were from MS1 and MS2.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the medical school's faculty and students.

  • May combine two claims.

    Findingown data

    Students were significantly more familiar with GAI tools than faculty; 76% of students versus 57% of faculty agreed or strongly agreed they were familiar with GAI tools.

    “Students were significantly more familiar with GAI tools than faculty. Combining “agree” and “strongly agree,” 76% of students were familiar with GAI tools versus 57% of faculty (Figure 1).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey respondents are representative of the medical school's faculty and students.
    • Rests on an assumption: The survey instrument validly measures AI experience, usage patterns, attitudes, and training preferences.

  • May combine two claims.

    Findingown data

    Faculty rated formal online training highest (59%) but indicated they would best learn AI from most key modalities.

    “Faculty rated formal online training the highest (59%) but indicated they would best learn AI from most of the key modalities.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the broader NYITCOM faculty and student population with respect to AI experience and training preferences.

  • May combine two claims.

    Findingown data

    The three most popular faculty resource priorities were hands-on training with AI skills (61%), guidelines for academic integrity and use of AI (48%), and a dedicated protected faculty development day to learn AI.

    “In order of preference, the 3 most popular items were “hands-on training with AI skills” (61%), the development of guidelines for academic integrity and use of AI (48%), and a dedicated, protected faculty development day to learn AI.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the broader NYITCOM faculty and student population with respect to AI experience and training preferences.

  • May combine two claims.

    Findingnone given

    Both groups had a substantial amount of concern about the validity of AI output, with faculty registering considerably more concern.

    “Both groups had a substantial amount of concern about the validity of AI output, with faculty registering considerably more concern.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey respondents are representative of the medical school's faculty and students.
    • Rests on an assumption: The survey instrument validly measures AI experience, usage patterns, attitudes, and training preferences.

  • May combine two claims.

    Findingown data

    Students reported being more comfortable with AI than faculty, but not significantly so.

    “Students reported being more comfortable with AI, but not significantly so.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the medical school's faculty and students.
    • Rests on an assumption: The survey instrument validly measures AI experience, usage patterns, attitudes, and training preferences.

  • May combine two claims.

    Findingown data

    Both groups used AI to some degree for personal, teaching, and research, but far fewer used it in clinical settings with patients (2%).

    “Both groups used AI to some degree for personal, teaching, and research, but far fewer in clinical settings with patients (2%).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the medical school's faculty and students.
    • Rests on an assumption: The survey instrument validly measures AI experience, usage patterns, attitudes, and training preferences.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    The survey instruments were revised using a Delphi process with the AITF and research team over 8 iterations, and 4 domains emerged.

    “The instruments were revised using a Delphi process with the AITF and research team over 8 iterations, 4 domains emerged (Table 1).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    Data saturation was achieved since there were no major changes in overall trends toward the end of data collection.

    “Data saturation was achieved since there were no major changes in overall trends toward the end of data collection.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Study design and conductnone given

    The sample captured a comprehensive perspective, including participants from all student levels and all faculty categories.

    “The sample captured a comprehensive perspective, including participants from all student levels and all faculty categories.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    For survey questions Q5, Q6, Q8, and Q10, t-tests were used to assess statistical significance of differences between group means, appropriate given large sample size and normality test results.

    “For survey questions Q5, Q6, Q8, and Q10, similar to Likert scale items, t-tests were used to assess the statistical significance of differences between group means. This method assumes a normal data distribution and is appropriate given the large sample size and normality test results, allowing for reliable group comparisons.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    To ensure data saturation, target response rates of 30% for students and >40% for faculty were set prior to distribution, and these targets were met.

    “To ensure data saturation, prior to distributing the survey, the research team set target response rates of 30% for students and >40% for the faculty group. These targets were met.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    The survey achieved a 50% response rate for faculty and 30% for students.

    “Specifically, 68 faculty members and 506 students participated, resulting in 50% and 30% response rates, respectively.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Both survey forms were pilot-tested to ensure they functioned effectively before electronic distribution.

    “Finally, VR, DN, and MS pilot-tested both forms to ensure they functioned effectively before electronic distribution.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Information about the experience of medical school faculty and students with GAI tools and how they used them was unknown.

    “However, information about the experience of medical school faculty and students with these tools and how they used them was unknown.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The study acknowledges limitations in directly correlating current usage with appropriate or responsible use.

    “We acknowledge the limitations in directly correlating current usage with appropriate or responsible use.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The study uses the data points as a starting layer for understanding engagement with AI, not as a definitive assessment of competence or readiness for clinical applications.

    “Our study uses these data points as a starting layer for understanding engagement with AI, not as a definitive assessment of competence or readiness for clinical applications.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The sampling design was guided by practical limitations inherent in distributing a voluntary survey within the medical education program.

    “The sampling design for our study was guided by the practical limitations inherent in distributing a voluntary survey within our medical education program.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the medical school's faculty and students.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    Several GAI tools rapidly evolved during the 3 months of the survey data collection, coinciding with a period of user experimentation with AI technologies at the medical school.

    “Further, several GAI tools rapidly evolved during the 3 months of the survey data collection, coinciding with a period of user experimentation with AI technologies at the medical school.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The study did not perform an a priori power analysis to determine the optimal sample size.

    “We did not perform an a priori power analysis to determine the optimal sample size for this study.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The generational technology-exposure divide has substantial implications for both curriculum design and faculty development.

    “This divide has substantial implications for both curriculum design and faculty development.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    This study was conducted at a single medical school, so the results are not easily generalizable.

    “This study was conducted at a single medical school, so the results are not easily generalizable.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    Simply because AI tools are being utilized does not inherently imply they are being used effectively or ethically, nor does it guarantee that students are prepared to extend this use into clinical practice.

    “Simply because AI tools are being utilized does not inherently imply they are being used effectively or ethically, nor does it guarantee that students are prepared to extend this use into clinical practice.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    Having some experience with a few AI tools does not necessarily indicate responsible use or imply that students or faculty can appropriately apply AI in clinical practice.

    “Furthermore, having some experience with a few AI tools does not necessarily indicate responsible use, nor does it imply students or faculty can appropriately apply AI in clinical practice.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A 2024 Elsevier study of 3000 clinicians and researchers reported that 11% were very familiar with AI.

    “a 2024 Elsevier study of 3000 clinicians and researchers reported that 11% were very familiar with AI.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Elsevier. Insights 2024: attitudes toward AI. 2024: 44. https://efaidnbmnnnibpcajpcglclefindmkaj/https://assets.ctfassets.net/o78em1y1w4i4/6BWRibyJNQLYkKWwKw7SVf/64c04b53ca9cc0795ac811f583f7eebb/Insights_2024_Attitudes_To_AI_Full_Report.pdf

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Interpretationown data

    The bell curve distribution of AI use among faculty and students underscores the need for a curriculum that addresses varying levels of AI familiarity.

    “The “bell curve” distribution of AI use among faculty and students underscores the need for a curriculum that addresses varying levels of AI familiarity.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Since the time of the survey, respondents' current understanding and/or usage of AI may have improved compared to survey outcomes.

    “Therefore, since the time of the survey, respondents’ current understanding and/or usage of AI may have improved compared to survey outcomes.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The finding that 56% of faculty and 70% of students used AI for teaching/learning tasks highlights both the potential use of AI for these tasks and the need for further training.

    “highlighting both the potential use of AI for these tasks and the need for further training.”
    Quote in the paperSubstantial claim
42 of 42

  • The paper relies on this without support.

    The survey respondents are representative of the broader NYITCOM faculty and student population with respect to AI experience and training preferences.

    “The sample captured a comprehensive perspective, including participants from all student levels and all faculty categories.”

  • The paper relies on this without support.

    Likert-scale responses can be treated as interval/continuous data for t-tests.

    “For survey questions Q5, Q6, Q8, and Q10, similar to Likert scale items, t-tests were used to assess the statistical significance of differences between group means.”

  • The authors state this as an assumption.

    The data are normally distributed.

    “This method assumes a normal data distribution and is appropriate given the large sample size and normality test results, allowing for reliable group comparisons.”

  • The paper relies on this without support.

    The two survey forms (faculty and student) measure the same constructs comparably enough to compare group means.

    “Researchers LM and MS designed 2 survey forms to compare faculty and student perceptions: (1) Medical Faculty AI Experience Survey 2024: 15 items, and (2) Medical Student AI Experience Survey 2024: 14 items.”

  • The paper relies on this without support.

    Non-respondents do not differ systematically from respondents in AI experience and training preferences.

    “The reported response rates for each group—faculty and students—were calculated by dividing the number of completed surveys by the total sample population. Specifically, 68 faculty members and 506 students participated, resulting in 50% and 30% response rates, respectively.”

  • The paper relies on this without support.

    The survey respondents are representative of the medical school's faculty and students.

    “The survey was made available to all faculty and students to participate from both the campus sites.”
    “The sampling design for our study was guided by the practical limitations inherent in distributing a voluntary survey within our medical education program.”

  • The paper relies on this without support.

    The survey instrument validly measures AI experience, usage patterns, attitudes, and training preferences.

    “we conducted an electronic survey to assess medical school faculty and students’ AI experience, usage patterns, and attitudes.”

  • The paper relies on this without support.

    Many basic science faculty members are not clinically active, which may affect their understanding of AI/GAI use in patient care responsibilities of medical students during clinical rotations.

    “Many basic science faculty members are not clinically active, which may affect their understanding of AI/GAI use in patient care responsibilities of medical students during clinical rotations.”

  • The paper relies on this without support.

    The relevance of certain options, like clinical interaction, varies between and within groups based on their clinical roles and exposure.

    “Additionally, the relevance of certain options, like clinical interaction, varies between and within groups based on their clinical roles and exposure.”
9 of 9

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

This check didn’t finish.

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. Ma Y Song Y Balch JA et al. Promoting AI competencies for medical students: a scoping review. ArXivorg. 2024:24.
  2. Elsevier. Insights 2024: attitudes toward AI. 2024:44. https://efaidnbmnnnibpcajpcglclefindmkaj/https://assets.ctfassets.net/o78em1y1w4i4/6BWRibyJNQLYkKWwKw7SVf/64c04b53ca9cc0795ac811f583f7eebb/Insights_2024_Attitudes_To_AI_Full_Report.pdf
  3. Rogers E, Medina U, Rivera M, Wiley C. Complex adaptive systems and the diffusion of innovation. Int J Innov. 2005;10(3):26.
  4. Elsevier. World class clinical information meets artificial intelligence: Clinicalkey.ai. Elsevier. Accessed 7.21.24. 2024. https://www.elsevier.com/promotions/clinicalkey-ai-ga1?utm_campaign=US_ClinicalKey+AI+Launch2202NA240021NA&utm_source=PSCID_NA&utm_medium=PS&utm_content=Branded&campid=2202NA240021&dgcid=HS_NA_US_ClinicalKey+AI+Launch_2202NA240021_PSCID_PS_CS&2202NA240021&gad_source=1&gclid=CjwKCAjw4_K0BhBsEiwAfVVZ_zJLW2zluIMIQzKv5CO4OgpJQ9rELHoK8kqiBsfMV_tdQGjnTNyiKhoCTpkQAvD_BwE.

Cited by

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Related

Authors
McCoy, L., Ganesan, N., Rajagopalan, V., McKell, D., Niño, D. F., & Swaim, M. C.
Affiliations
  • New York Institute of Technology (US)
  • Department of Biomedical and Anatomical Sciences, NYITCOM, Jonesboro, USA
  • Thomas Jefferson University (US)
  • The University of Texas at Tyler (US)
Corresponding author
McCoy, Lise
ORCID
Venue
Journal of Medical Education and Curricular Development
Publisher
SAGE Publications
Published
2025-05-14
Volume
12
Pages
23821205251339226-23821205251339226
ISSN
2382-1205 (ISSN-L)
DOI
10.1177/23821205251339226
PMC
PMC12078978
OpenAlex
W4410351887
Cited by
30
References
37
Citations by year
2026: 14 · 2025: 15
FWCI
6.72
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Medical Education and Curricular Development (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
generative AI, artificial intelligence in medical education, AI training needs, faculty perspectives, medical students, AI adoption readiness, AI ethics, undergraduate medical education

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-15.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-15.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

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