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Related Concept Videos

Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis01:24

Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis

The nursing process provides a clinical decision-making framework for patients and families to establish and implement a personalized care plan. Since part of the nurse's duties is to teach patients, the steps of the nursing process are the most effective way to approach instruction. The nursing process and the teaching-learning process are inextricably linked.
It is critical to determine the patient's learning needs during the assessment. Determination of learning needs compounds data from the...
Nursing Assessment01:29

Nursing Assessment

The two sources for collecting information are primary and secondary. After gathering information, interpretation and validation help to complete the data. The purpose of assessment is to establish data with the initial information, to interpret data about the patient's perceived needs and health problems, and to respond to these problems identified.
The nurse collects all aspects of the patient's health in the initial assessment, establishing priorities for ongoing focused assessments and...
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...

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Related Experiment Videos

AI-Assisted Formative Assessment in Clinical Education: From Algorithms to Agency.

Quang Thanh Nguyen1,2,3, Thuy Minh Ha4, Tina Mai5

  • 1College of Health Sciences, VinUniversity, Da Ton, Gia Lam, Hanoi, Hanoi, 100000, Vietnam, 84 89902123.

JMIR Medical Education
|June 4, 2026
PubMed
Summary

Artificial intelligence (AI) enhances clinical education by providing instant feedback for medical students. However, effective AI-assisted formative assessment requires critical interpretation by learners and educators, not just automated scoring.

Keywords:
AIartificial intelligenceclinical educationfeedbackformative assessmentmedical education

Related Experiment Videos

Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Educational Technology

Background:

  • Artificial intelligence (AI) is transforming clinical education by integrating assessment and feedback into learning.
  • AI tools like machine learning dashboards and large language models offer personalized practice and rapid feedback for medical students.
  • The mere increase in data and feedback does not guarantee improved learning outcomes.

Purpose of the Study:

  • To define AI-assisted formative assessment as the intentional use of AI for learning feedback, not grading.
  • To explore the potential and risks of AI in clinical education for educators and leaders.
  • To provide evidence-informed design propositions for implementing AI in various clinical learning contexts.

Main Methods:

  • Synthesizing the current evidence base on AI in clinical education.
  • Examining key risks associated with AI, including hallucination and automation bias.
  • Presenting context-specific implementation examples and practical implications for faculty and institutions.

Main Results:

  • AI offers scale, adaptivity, and conversational simulation for formative assessment.
  • AI outputs require critical interpretation by learners and educators to be effective.
  • Current evidence is early, heterogeneous, and concentrated in short-term studies.
  • Key risks include hallucination, automation bias, and data privacy concerns.

Conclusions:

  • The value of AI-assisted formative assessment hinges on educational design that prioritizes learner agency and professional judgment.
  • AI tools should support, not replace, human accountability and critical interpretation in education.
  • Implementation requires careful consideration of risks, faculty development, and institutional governance.