CoChat
Sign Up
Cited by 96Artificial Intelligence in Healthcare and EducationArtificial Intelligence in EducationInnovations in Medical Education

Navigating the Pedagogical Landscape: Exploring the Implications of AI and Chatbots in Nursing Education

Open access
Srinivasan, Muthuvenkatachalam, Venugopal, Ambili, Venkatesan, Latha, Kumar, Rajesh·2024·JMIR Nursing
DOI 10.2196/52105PMC PMC11211702

Published version of a preprint

Open this paper in CoChat — free account

Abstract

This viewpoint paper explores the pedagogical implications of artificial intelligence (AI) and AI-based chatbots such as ChatGPT in nursing education, examining their potential uses, benefits, challenges, and ethical considerations. AI and chatbots offer transformative opportunities for nursing education, such as personalized learning, simulation and practice, accessible learning, and improved efficiency. They have the potential to increase student engagement and motivation, enhance learning outcomes, and augment teacher support. However, the integration of these technologies also raises ethical considerations, such as privacy, confidentiality, and bias. The viewpoint paper provides a comprehensive overview of the current state of AI and chatbots in nursing education, offering insights into best practices and guidelines for their integration. By examining the impact of AI and ChatGPT on student learning, engagement, and teacher effectiveness and efficiency, this review aims to contribute to the ongoing discussion on the use of AI and chatbots in nursing education and provide recommendations for future research and development in the field.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/52105

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Limited hands-on evaluation methods and overreliance risks further challenge effective implementation.

    “Limited hands-on evaluation methods and overreliance risks further challenge effective implementation”

    From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Srinivasan et al., 2024

    The Limitations section (page unavailable) warns that AI and chatbots cannot replace human interaction or the development of interpersonal skills, and that their errors require cautious use. These points support a risk from overreliance, but our copy does not identify limited hands-on evaluation methods as an implementation challenge. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Emerging evidence supports the role of AI in supporting metacognitive processes, adaptive reasoning, and structured learning.

    “The findings of this study also complement emerging evidence on the role of AI in supporting metacognitive processes, adaptive reasoning, and structured learning (Han et al., 2025;Labrague & Al Sabei, 2024;Srinivasan et al., 2024).”

    From AI-assisted concept mapping enhances orthopaedic nursing students’ critical thinking: A quasi-experimental study (2026) · cites this paper as Srinivasan et al., 2024

    The Pedagogical Implications section says AI-supported feedback, tailored assessments, and simulations can improve students’ problem-solving skills. The Teaching Effectiveness and Efficiency section describes adaptive learning systems that tailor materials to students’ needs. These passages support personalized and guided learning, but our copy does not specifically address support for metacognitive processes or adaptive reasoning by learners; its discussion of adaptation concerns the teaching system. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • AI has the potential to function not as a substitute for human reasoning but as a scaffold that reinforces analytical engagement.

    “These studies highlight the potential of AI to function not as a substitute for human reasoning but as a scaffold that reinforces analytical engagement-an important consideration in orthopaedic nursing, where students must integrate musculoskeletal assessment data, pathophysiology, and clinical judgment.”

    From AI-assisted concept mapping enhances orthopaedic nursing students’ critical thinking: A quasi-experimental study (2026) · cites this paper as Srinivasan et al., 2024

Self-check

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

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Srinivasan, M., Venugopal, A., Venkatesan, L., & Kumar, R.
Affiliations
  • All India Institute of Medical Sciences, Mangalagiri (IN)
  • All India Institute of Medical Sciences (IN)
  • All India Institute of Medical Sciences Rishikesh (IN)
Corresponding author
Srinivasan, Muthuvenkatachalam
ORCID
Venue
JMIR Nursing
Publisher
JMIR Publications Inc.
Published
2024-06-13
Volume
7
Pages
e52105
ISSN
2562-7600 (ISSN-L)
DOI
10.2196/52105
PMC
PMC11211702
OpenAlex
W4389630687
Cited by
96
References
16
Citations by year
2026: 45 · 2025: 46 · 2024: 5
FWCI
3.82
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Nursing (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
Keywords
artificial intelligence in education, AI chatbots, ChatGPT, nursing education, personalized learning, simulation-based training, student engagement, ethical considerations, data privacy, algorithmic bias, teacher support, technology integration in pedagogy
MeSH terms
Artificial Intelligence, Education, Nursing, Humans, Students, Nursing

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review