Evidence map›Paper›PMID 42090298›Full record

ArticleJMIR medical education2026

Design and Evaluation of a Faculty Development Workshop Series on Integrating Generative Artificial Intelligence in Medical Education: Mixed Methods Pilot Study.

Rajalakshmi Anand, Nicole Bowers, Mange Festo Manyama

Abstract read
In one paragraph

Article in JMIR medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Rajalakshmi Anand *Institute for Population Health, Weill Cornell Medical College-Qatar, Qatar Foundation-Education City, Doha, Baladīyat ad Dawḩah, 24144, Qatar, 974 44928389.ORCID 0009-0000-3841-4004
Nicole Bowers *Mary Lou Fulton College for Teaching and Learning Innovation, Arizona State University, Tempe, AZ, United States.ORCID 0000-0001-9408-6885
Mange Festo Manyama *Division of Medical Education, Weill Cornell Medical College-Qatar, Doha, Qatar.ORCID 0000-0001-5905-0027

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Generative artificial intelligence (GenAI) tools are being increasingly applied to teaching and learning in medical education creating both instructional opportunities and pedagogical challenges. While GenAI offers potential to enhance teaching, assessment, and curriculum design, many medical faculty lack structured guidance on how to integrate these tools ethically and pedagogically within discipline-specific, high-stakes educational contexts. Objective: This study aimed to design, implement, and evaluate a faculty development workshop series for ethical and pedagogical integration of GenAI in medical education teaching. Methods: A mixed methods pilot study was conducted to design, implement, and evaluate a faculty development workshop series "Professional Development in Generative Artificial Intelligence for Pedagogy" at Weill Cornell Medicine-Qatar, a US medical school in Qatar. The program consisted of five 1-hour synchronous online workshops grounded in Experiential Learning Theory and the Technological Pedagogical Content Knowledge framework. Ten medical faculty from multiple disciplines participated. Quantitative data were collected through an online preintervention survey, an online postintervention survey with open-ended questions, and an online 2-week follow-up survey. Surveys consisted of 5-point Likert scale items capturing perceptions of workshop quality, confidence, and intended application. Qualitative data included full workshop transcripts, facilitator theoretical notes, and facilitator memos. Descriptive statistics summarized quantitative findings, while qualitative data were analyzed using a combination of deductive and inductive coding, alongside narrative analysis. Findings were integrated to generate convergent interpretations. Results: Qualitative analysis of workshop transcripts suggested evolving engagement with GenAI, with participants describing movement from exploratory use toward more intentional pedagogical application. Postintervention survey results indicated high satisfaction with program content, organization, relevance, and overall quality. Two-week follow-up survey responses (n=5) suggested increased self-reported confidence in applying GenAI tools, and perceived shifts in how participants conceptualized teaching with GenAI. Faculty described intended strategies for integrating GenAI into lesson planning, assessment design, visualization of learning materials, and case-based instruction, while emphasizing the importance of human oversight, critical appraisal, and ethical judgment. Findings highlighted the perceived value of hands-on experimentation, reflective discussion, and adaptive facilitation in supporting early faculty engagement. Conclusions: This pilot study provides early evidence that an experiential, theory-informed, and adaptively facilitated faculty development workshop series may support medical faculty in developing self-reported confidence, awareness, and initial strategies for responsible GenAI integration. Findings are exploratory and limited by a small sample size, a single institution, and reliance on self-reported data. Nevertheless, the Professional Development in Generative Artificial Intelligence for Pedagogy workshop series presents a flexible and theory-informed faculty development approach that may inform future faculty development initiatives in medical education as GenAI technologies continue to evolve.

Indexed as

EducationEducation, MedicalFaculty, MedicalGenerative Artificial IntelligenceStaff DevelopmentCurriculumFemaleHumansMalePilot ProjectsQatarSurveys and Questionnairesethical AIexperiential learningfaculty developmentgenerative artificial intelligencemedical educationresponsible AITPACK

Identifiers

PMID42090298
PMCPMC13148327

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.