Academic Journal
View in EDS

Feasibility of AI and Human Standardized Patients to Enhance Customer Discovery Communication Skills in Medical Students: Preliminary Evaluation of an Observational Cohort Study.

Bibliographic Details
Title: Feasibility of AI and Human Standardized Patients to Enhance Customer Discovery Communication Skills in Medical Students: Preliminary Evaluation of an Observational Cohort Study.
Authors: Hafer N; University of Massachusetts Chan Medical School, Worcester, MA, United States., Chitre M; University of Massachusetts Chan Medical School, Worcester, MA, United States., Fischer M; University of Massachusetts Chan Medical School, Worcester, MA, United States., LeMoine J; University of Massachusetts Chan Medical School, Worcester, MA, United States.; My Diji Helpers, Inc, Watertown, MA, United States.
Source: JMIR formative research [JMIR Form Res] 2026 Sep 10; Vol. 10, pp. e95225. Date of Electronic Publication: 2026 Sep 10.
Publication Type: Journal Article; Observational Study
Language: English
Journal Info: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101726394 Publication Model: Electronic Cited Medium: Internet ISSN: 2561-326X (Electronic) Linking ISSN: 2561326X NLM ISO Abbreviation: JMIR Form Res Subsets: MEDLINE
Imprint Name(s): Original Publication: Toronto, ON, Canada : JMIR Publications, [2017]-
MeSH Terms: Students, Medical*/statistics & numerical data , Students, Medical*/psychology , Patient Simulation* , Communication* , Artificial Intelligence*, Humans ; Cohort Studies ; Feasibility Studies ; Male ; Female ; Adult ; Generative Artificial Intelligence
Abstract: Background: Medical students must build skills beyond traditional clinical domains to best shape the evolving health care system and fill a variety of professional roles after graduation. AI offers new methods for medical education.
Objective: This study aimed to develop and evaluate the feasibility and preliminary effectiveness of traditional (human) standardized patient (SP) engagement and generative AI (GenAI)-generated SP engagement as a low-stakes way for students to practice customer discovery interviews.
Methods: An interactive classroom experience was created in the UMass Chan Medical School interprofessional Center for Experiential Learning and Simulation (iCELS) using an observational cohort design to simulate 2 different interview scenarios with human SPs and AI-generated SPs. The simulation with human SPs was conducted with 74 first-year medical students in the Entrepreneurship, Biodesign, and Innovation Pathway starting in 2023. In 2026, 2 GenAI chatbot interview agents, using OpenAI GPT-5.2, were developed and added as an additional part of the simulation experience. The 2 scenario-specific GenAI agents (patient and clinician/physician) used structured instructions defining their roles, interview contexts, knowledge boundaries, response styles, time limits, feedback, and scoring rubric. The simulations took place over a single 2-hour session in the spring semester. Interview scenarios, sample questions and answers, and an evaluation rubric were developed for both SPs to promote consistency. Primary outcomes were student attitudes regarding the acceptability of the simulation and self-efficacy, collected via a validated online survey immediately after class. Students were asked to rate statements on a 3-point (1="not at all relevant," 2="somewhat relevant," and 3="very relevant"), 4-point (1="strongly disagree" to 4="strongly agree"), or 5-point (1="strongly disagree," 3="neutral," and 5="strongly agree") Likert scale and were also allowed to provide open-ended comments.
Results: The students gave the simulation with human SPs high scores, with 85% (23/27) agreeing or strongly agreeing that the exercise met learning objectives, with a median score of 4 (IQR 1) on a 4-point Likert scale. Responses to the AI chatbot session had a bimodal distribution; for example, 65% (13/20) of students agreed that "This experience with an AI chatbot SP helped me practice my customer discovery interview skills" while 35% (7/20) disagreed with this statement. A Mann-Whitney U test compared responses between the human SP and AI chatbot groups and revealed no significant difference in responses.
Conclusions: This preliminary study with an AI chatbot was able to replicate realistic customer discovery interviews in 2 different scenarios. Larger studies are needed to determine the feasibility and acceptability of these chatbots for practicing customer discovery skills. Future work will create transcripts of both the AI chatbot and human SP interviews and use independent raters to score interview quality based on our scoring rubric. Finally, we plan to enhance the chatbots to provide a more immersive and realistic experience for students.
(©Nathaniel Hafer, Monika Chitre, Melissa Fischer, Julie LeMoine. Originally published in JMIR Formative Research (https://formative.jmir.org), 10.09.2026.)
References: JMIR Med Educ. 2025 May 20;11:e63353. (PMID: 40393017)
JMIR Med Educ. 2026 Mar 25;12:e90368. (PMID: 41881044)
JMIR Med Educ. 2026 Jun 4;12:e93710. (PMID: 42241685)
JMIR Med Educ. 2024 Sep 23;10:e58753. (PMID: 39312284)
BMC Med Educ. 2025 Nov 24;25(1):1755. (PMID: 41286823)
Front Digit Health. 2026 Jun 10;8:1830254. (PMID: 42359442)
JMIR Med Educ. 2023 Aug 14;9:e50945. (PMID: 37578830)
Acad Med. 2026 Oct 1;101(10):1265-1273. (PMID: 42391038)
JMIR Med Educ. 2023 Jun 1;9:e48291. (PMID: 37261894)
BMC Med Educ. 2025 Aug 30;25(1):1227. (PMID: 40885939)
Med Educ. 2024 Nov;58(11):1276-1285. (PMID: 38639098)
Med Educ Online. 2023 Dec;28(1):2228550. (PMID: 37347808)
J Biomed Inform. 2009 Apr;42(2):377-81. (PMID: 18929686)
JMIR Form Res. 2026 Jan 19;10:e82828. (PMID: 41553752)
J Dent Educ. 2026 Apr 20;:. (PMID: 42003540)
J Clin Transl Sci. 2025 Jul 24;9(1):e172. (PMID: 40979109)
J Med Educ Curric Dev. 2022 May 12;9:23821205221091511. (PMID: 35592131)
JMIR Med Educ. 2026 Apr 20;12:e87012. (PMID: 42008615)
Int J Med Inform. 2026 Nov 1;220:106589. (PMID: 42400960)
J Biomed Inform. 2019 Jul;95:103208. (PMID: 31078660)
Med Teach. 2026 Jun;48(6):1012-1021. (PMID: 41518042)
Contributed Indexing: Keywords: AI; GenAI; I-corps; academic success; artificial intelligence; entrepreneurship; generative artificial intelligence; interview; simulation training; undergraduate medical education
Entry Date(s): Date Created: 20260910 Date Completed: 20260910 Latest Revision: 20260927
Update Code: 20260927
PubMed Central ID: PMC13613044
DOI: 10.2196/95225
PMID: 42721453
Database: MEDLINE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:cmedm&genre=article&issn=2561326X&ISBN=&volume=10&issue=&date=20260910&spage=e95225&pages=e95225&title=JMIR formative research&atitle=Feasibility%20of%20AI%20and%20Human%20Standardized%20Patients%20to%20Enhance%20Customer%20Discovery%20Communication%20Skills%20in%20Medical%20Students%3A%20Preliminary%20Evaluation%20of%20an%20Observational%20Cohort%20Study.&aulast=Hafer%20N&id=DOI:10.2196/95225
    Name: Full Text Finder (for New FTF UI) (ns324271)
    Category: fullText
    Text: Full Text Finder
    MouseOverText: Full Text Finder
Header DbId: cmedm
DbLabel: MEDLINE
An: 42721453
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Feasibility of AI and Human Standardized Patients to Enhance Customer Discovery Communication Skills in Medical Students: Preliminary Evaluation of an Observational Cohort Study.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Hafer+N%22">Hafer N</searchLink>; University of Massachusetts Chan Medical School, Worcester, MA, United States.<br /><searchLink fieldCode="AU" term="%22Chitre+M%22">Chitre M</searchLink>; University of Massachusetts Chan Medical School, Worcester, MA, United States.<br /><searchLink fieldCode="AU" term="%22Fischer+M%22">Fischer M</searchLink>; University of Massachusetts Chan Medical School, Worcester, MA, United States.<br /><searchLink fieldCode="AU" term="%22LeMoine+J%22">LeMoine J</searchLink>; University of Massachusetts Chan Medical School, Worcester, MA, United States.; My Diji Helpers, Inc, Watertown, MA, United States.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101726394%22">JMIR formative research</searchLink> [JMIR Form Res] 2026 Sep 10; Vol. 10, pp. e95225. <i>Date of Electronic Publication: </i>2026 Sep 10.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article; Observational Study
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22JMIR+Publications%22">JMIR Publications </searchLink><i>Country of Publication: </i>Canada <i>NLM ID: </i>101726394 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2561-326X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%222561326X%22">2561326X </searchLink><i>NLM ISO Abbreviation: </i>JMIR Form Res <i>Subsets: </i>MEDLINE
– Name: PublisherInfo
  Label: Imprint Name(s)
  Group: PubInfo
  Data: <i>Original Publication</i>: Toronto, ON, Canada : JMIR Publications, [2017]-
– Name: SubjectMESH
  Label: MeSH Terms
  Group: Su
  Data: <searchLink fieldCode="MM" term="%22Students%2C+Medical%22">Students, Medical*</searchLink>/<searchLink fieldCode="MM" term="%22Students%2C+Medical+statistics+%26+numerical+data%22">statistics & numerical data</searchLink> <br /><searchLink fieldCode="MM" term="%22Students%2C+Medical%22">Students, Medical*</searchLink>/<searchLink fieldCode="MM" term="%22Students%2C+Medical+psychology%22">psychology</searchLink> <br /><searchLink fieldCode="MM" term="%22Patient+Simulation%22">Patient Simulation*</searchLink> <br /><searchLink fieldCode="MM" term="%22Communication%22">Communication*</searchLink> <br /><searchLink fieldCode="MM" term="%22Artificial+Intelligence%22">Artificial Intelligence*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Cohort+Studies%22">Cohort Studies</searchLink> ; <searchLink fieldCode="MH" term="%22Feasibility+Studies%22">Feasibility Studies</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Adult%22">Adult</searchLink> ; <searchLink fieldCode="MH" term="%22Generative+Artificial+Intelligence%22">Generative Artificial Intelligence</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Medical students must build skills beyond traditional clinical domains to best shape the evolving health care system and fill a variety of professional roles after graduation. AI offers new methods for medical education.<br />Objective: This study aimed to develop and evaluate the feasibility and preliminary effectiveness of traditional (human) standardized patient (SP) engagement and generative AI (GenAI)-generated SP engagement as a low-stakes way for students to practice customer discovery interviews.<br />Methods: An interactive classroom experience was created in the UMass Chan Medical School interprofessional Center for Experiential Learning and Simulation (iCELS) using an observational cohort design to simulate 2 different interview scenarios with human SPs and AI-generated SPs. The simulation with human SPs was conducted with 74 first-year medical students in the Entrepreneurship, Biodesign, and Innovation Pathway starting in 2023. In 2026, 2 GenAI chatbot interview agents, using OpenAI GPT-5.2, were developed and added as an additional part of the simulation experience. The 2 scenario-specific GenAI agents (patient and clinician/physician) used structured instructions defining their roles, interview contexts, knowledge boundaries, response styles, time limits, feedback, and scoring rubric. The simulations took place over a single 2-hour session in the spring semester. Interview scenarios, sample questions and answers, and an evaluation rubric were developed for both SPs to promote consistency. Primary outcomes were student attitudes regarding the acceptability of the simulation and self-efficacy, collected via a validated online survey immediately after class. Students were asked to rate statements on a 3-point (1="not at all relevant," 2="somewhat relevant," and 3="very relevant"), 4-point (1="strongly disagree" to 4="strongly agree"), or 5-point (1="strongly disagree," 3="neutral," and 5="strongly agree") Likert scale and were also allowed to provide open-ended comments.<br />Results: The students gave the simulation with human SPs high scores, with 85% (23/27) agreeing or strongly agreeing that the exercise met learning objectives, with a median score of 4 (IQR 1) on a 4-point Likert scale. Responses to the AI chatbot session had a bimodal distribution; for example, 65% (13/20) of students agreed that "This experience with an AI chatbot SP helped me practice my customer discovery interview skills" while 35% (7/20) disagreed with this statement. A Mann-Whitney U test compared responses between the human SP and AI chatbot groups and revealed no significant difference in responses.<br />Conclusions: This preliminary study with an AI chatbot was able to replicate realistic customer discovery interviews in 2 different scenarios. Larger studies are needed to determine the feasibility and acceptability of these chatbots for practicing customer discovery skills. Future work will create transcripts of both the AI chatbot and human SP interviews and use independent raters to score interview quality based on our scoring rubric. Finally, we plan to enhance the chatbots to provide a more immersive and realistic experience for students.<br /> (©Nathaniel Hafer, Monika Chitre, Melissa Fischer, Julie LeMoine. Originally published in JMIR Formative Research (https://formative.jmir.org), 10.09.2026.)
– Name: Ref
  Label: References
  Group: RefInfo
  Data: JMIR Med Educ. 2025 May 20;11:e63353. (PMID: <searchLink fieldCode="PM" term="%2240393017%22">40393017)</searchLink><br />JMIR Med Educ. 2026 Mar 25;12:e90368. (PMID: <searchLink fieldCode="PM" term="%2241881044%22">41881044)</searchLink><br />JMIR Med Educ. 2026 Jun 4;12:e93710. (PMID: <searchLink fieldCode="PM" term="%2242241685%22">42241685)</searchLink><br />JMIR Med Educ. 2024 Sep 23;10:e58753. (PMID: <searchLink fieldCode="PM" term="%2239312284%22">39312284)</searchLink><br />BMC Med Educ. 2025 Nov 24;25(1):1755. (PMID: <searchLink fieldCode="PM" term="%2241286823%22">41286823)</searchLink><br />Front Digit Health. 2026 Jun 10;8:1830254. (PMID: <searchLink fieldCode="PM" term="%2242359442%22">42359442)</searchLink><br />JMIR Med Educ. 2023 Aug 14;9:e50945. (PMID: <searchLink fieldCode="PM" term="%2237578830%22">37578830)</searchLink><br />Acad Med. 2026 Oct 1;101(10):1265-1273. (PMID: <searchLink fieldCode="PM" term="%2242391038%22">42391038)</searchLink><br />JMIR Med Educ. 2023 Jun 1;9:e48291. (PMID: <searchLink fieldCode="PM" term="%2237261894%22">37261894)</searchLink><br />BMC Med Educ. 2025 Aug 30;25(1):1227. (PMID: <searchLink fieldCode="PM" term="%2240885939%22">40885939)</searchLink><br />Med Educ. 2024 Nov;58(11):1276-1285. (PMID: <searchLink fieldCode="PM" term="%2238639098%22">38639098)</searchLink><br />Med Educ Online. 2023 Dec;28(1):2228550. (PMID: <searchLink fieldCode="PM" term="%2237347808%22">37347808)</searchLink><br />J Biomed Inform. 2009 Apr;42(2):377-81. (PMID: <searchLink fieldCode="PM" term="%2218929686%22">18929686)</searchLink><br />JMIR Form Res. 2026 Jan 19;10:e82828. (PMID: <searchLink fieldCode="PM" term="%2241553752%22">41553752)</searchLink><br />J Dent Educ. 2026 Apr 20;:. (PMID: <searchLink fieldCode="PM" term="%2242003540%22">42003540)</searchLink><br />J Clin Transl Sci. 2025 Jul 24;9(1):e172. (PMID: <searchLink fieldCode="PM" term="%2240979109%22">40979109)</searchLink><br />J Med Educ Curric Dev. 2022 May 12;9:23821205221091511. (PMID: <searchLink fieldCode="PM" term="%2235592131%22">35592131)</searchLink><br />JMIR Med Educ. 2026 Apr 20;12:e87012. (PMID: <searchLink fieldCode="PM" term="%2242008615%22">42008615)</searchLink><br />Int J Med Inform. 2026 Nov 1;220:106589. (PMID: <searchLink fieldCode="PM" term="%2242400960%22">42400960)</searchLink><br />J Biomed Inform. 2019 Jul;95:103208. (PMID: <searchLink fieldCode="PM" term="%2231078660%22">31078660)</searchLink><br />Med Teach. 2026 Jun;48(6):1012-1021. (PMID: <searchLink fieldCode="PM" term="%2241518042%22">41518042)</searchLink>
– Name: SubjectMinor
  Label: Contributed Indexing
  Group:
  Data: <i>Keywords: </i>AI; GenAI; I-corps; academic success; artificial intelligence; entrepreneurship; generative artificial intelligence; interview; simulation training; undergraduate medical education
– Name: DateEntry
  Label: Entry Date(s)
  Group: Date
  Data: <i>Date Created: </i>20260910 <i>Date Completed: </i>20260910 <i>Latest Revision: </i>20260927
– Name: DateUpdate
  Label: Update Code
  Group: Date
  Data: 20260927
– Name: PubmedCentralID
  Label: PubMed Central ID
  Group: ID
  Data: PMC13613044
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.2196/95225
– Name: AN
  Label: PMID
  Group: ID
  Data: 42721453
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=42721453
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.2196/95225
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: e95225
    Subjects:
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Cohort Studies
        Type: general
      – SubjectFull: Feasibility Studies
        Type: general
      – SubjectFull: Male
        Type: general
      – SubjectFull: Female
        Type: general
      – SubjectFull: Adult
        Type: general
      – SubjectFull: Generative Artificial Intelligence
        Type: general
      – SubjectFull: Students, Medical statistics & numerical data
        Type: general
      – SubjectFull: Students, Medical psychology
        Type: general
      – SubjectFull: Patient Simulation
        Type: general
      – SubjectFull: Communication
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
    Titles:
      – TitleFull: Feasibility of AI and Human Standardized Patients to Enhance Customer Discovery Communication Skills in Medical Students: Preliminary Evaluation of an Observational Cohort Study.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Hafer N
      – PersonEntity:
          Name:
            NameFull: Chitre M
      – PersonEntity:
          Name:
            NameFull: Fischer M
      – PersonEntity:
          Name:
            NameFull: LeMoine J
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 10
              M: 09
              Text: 2026 Sep 10
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 2561-326X
          Numbering:
            – Type: volume
              Value: 10
          Titles:
            – TitleFull: JMIR formative research
              Type: main
ResultId 1