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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Evaluation of a Problem-Based Learning Program's Effect on Artificial Intelligence Ethics Among Japanese Medical

Yuma Ota1, Yoshikazu Asada2, Saori Kubo3

  • 1Medical Education Center, Jichi Medical University Graduate School, Shimotsuke, Japan.

JMIR Medical Education
|January 14, 2026
PubMed
Summary

A problem-based learning (PBL) program significantly improved medical students' knowledge of artificial intelligence (AI) legal and ethical issues. The educational intervention transformed abstract thinking into concrete, clinically grounded reasoning, highlighting PBL's effectiveness in AI ethics education.

Keywords:
artificial intelligencemedical ethicsmixed methods researchproblem-based learningtext miningundergraduate medical education

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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

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Area of Science:

  • Medical Education
  • Artificial Intelligence Ethics
  • Health Informatics

Background:

  • Medical school curricula lag in integrating artificial intelligence (AI) education.
  • Medical students show a gap between positive AI perceptions and actual competencies, particularly in legal and ethical understanding.
  • Existing AI education challenges include addressing regulatory and ethical complexities in clinical practice.

Purpose of the Study:

  • To evaluate an educational program's effectiveness in enhancing medical students' understanding of legal and ethical AI issues.
  • To assess the program's impact on knowledge acquisition and the qualitative shift in students' ethical reasoning.
  • To determine the contribution of lecture and problem-based learning (PBL) components.

Main Methods:

  • A mixed-methods, single-group pretest-posttest design with 118 fourth-year medical students.
  • A 1-day intervention including a lecture and PBL session on a clinical AI case.
  • Quantitative analysis via MCQ and qualitative analysis using descriptive essays and text mining.

Main Results:

  • Significant improvement in objective knowledge scores post-intervention (P<.001).
  • Qualitative analysis revealed a transformation from fragmented to integrated thinking networks regarding AI ethics.
  • Students increased their use of professional and ethical terminology, such as 'bias' and 'personal information.'

Conclusions:

  • PBL-based education effectively improved medical students' AI ethics knowledge and reasoning.
  • The program fostered a shift from abstract to concrete, clinically relevant ethical thinking.
  • PBL demonstrates potential as a key pedagogical method for AI ethics in medical education.