Evidence map›Paper›PMID 41401239›Full record

ArticleJMIR medical informatics2025

Incorporating Generative AI Into a Health Informatics Curriculum to Build 21st Century Competencies: Multisite Pre-Post Study.

Freddie Seba, Miriam Isola, Laura Mills, Mohan Zalake, Jacob Krive

Abstract readMulticenter Study
In one paragraph

Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
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

5 authors.

Freddie SebaMasters of Science in Digital Health Informatics School of Nursing and Health Professions, University of San Francisco, 2130 Fulton Street, San Francisco, CA, 94117, United States, 1 415-422-5555.ORCID 0009-0002-5916-2787
Miriam IsolaDepartment of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, IL, United States.ORCID 0000-0001-5687-6472
Laura MillsDepartment of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, IL, United States.ORCID 0009-0002-1117-2011
Mohan ZalakeDepartment of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, IL, United States.ORCID 0000-0002-6799-6784
Jacob KriveDepartment of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, IL, United States.ORCID 0000-0002-1259-717X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: We designed learning assignments for students to develop knowledge, skills, and professional attitudes about generative artificial intelligence (AI) in 2 different Master's level courses in health informatics. Our innovative approach assumed that the students had no technical background or experience in using generative AI tools. Objective: This study aims to offer generalizable methods and experiences on integration and assessment of generative AI content into the higher education's health informatics curricula. The study's central driver is the preparation of graduate students with generative AI tools, skills, ethical discernment, and critical thinking capacities aligned with the rapidly shifting job-market requirements, independent of graduate students' backgrounds and technical expertise. Methods: During the semester, students completed a pretest and posttest to assess knowledge about generative AI. Reflections explored their expectations and experiences using generative AI to complete their assignments and projects during the semester. Strong emphasis was placed on building skills and professional attitudes by using generative AI. Student engagement in behavioral, emotional, and cognitive domains was explored via detailed analysis of student reflections by faculty. Results: Students at the University of Illinois Chicago increased their knowledge about generative AI from 81% to 93% through research of the basic generative AI concepts, as evidenced from outcomes of the open-book pre-and posttests given at the beginning and end of the capstone course. University of San Francisco students also improved from 77% to 80% by the end of the semester. Faculty analysis of student reflections upon completion of the course revealed primary interests in the essentials of generative AI, AI transformations to information and knowledge, and organizational changes influenced by AI adoption in the health care organizations, with ethics being a primary driver of students' interests and engagement. Conclusions: Data from student reflections provided insight into generative AI skills that students developed and that health informatics programs can consider incorporating into their curricula. Building competencies in generative AI will prepare students for the 21st century workforce and enable them to build skills employers are seeking in the new digital health environment.

Indexed as

Artificial IntelligenceCurriculumMedical InformaticsChicagoHumansAIartificial intelligencecapstone experiencegenerative AIgenerative AI ethicsgenerative artificial intelligencepedagogical innovationstudent engagement

Identifiers

PMID41401239
PMCPMC12707436

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.