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Comparative accuracy of ChatGPT-4, Microsoft Copilot and Google Gemini in the Italian entrance test for healthcare sciences degrees: a cross-sectional study
by
Corradi, Federica
, Palese, Alvisa
, Rossettini, Giacomo
, Castellini, Greta
, Chiappinotto, Stefania
, Gianola, Silvia
, Turolla, Andrea
, Rodeghiero, Lia
, Cook, Chad
, Pillastrini, Paolo
in
Accuracy
/ Admission Criteria
/ Algorithms
/ Analysis
/ Artificial Intelligence
/ Chatbots
/ Cognition & reasoning
/ College entrance achievement tests
/ Colleges & universities
/ Comparative analysis
/ Computer software industry
/ Cross-Sectional Studies
/ Dentistry
/ Education
/ Educational Measurement - methods
/ Entrance examinations
/ Equipment and supplies
/ Ethics
/ Female
/ Health occupations
/ Humans
/ International economic relations
/ Italian
/ Italy
/ Learning
/ Licensing Examinations (Professions)
/ Male
/ Medical care
/ Medical colleges
/ Medical Education
/ Meta Analysis
/ Multiple choice
/ Nursing
/ Nursing education
/ Ophthalmology
/ Organic Chemistry
/ Physical therapy
/ Quality management
/ Science education
/ Science Instruction
/ Secondary Schools
/ Speech Therapy
/ Standardized Tests
/ Students
/ Teaching
/ Test Preparation
/ Tests, problems and exercises
/ Theory of Medicine/Bioethics
/ Universities
/ Universities and colleges
2024
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Comparative accuracy of ChatGPT-4, Microsoft Copilot and Google Gemini in the Italian entrance test for healthcare sciences degrees: a cross-sectional study
by
Corradi, Federica
, Palese, Alvisa
, Rossettini, Giacomo
, Castellini, Greta
, Chiappinotto, Stefania
, Gianola, Silvia
, Turolla, Andrea
, Rodeghiero, Lia
, Cook, Chad
, Pillastrini, Paolo
in
Accuracy
/ Admission Criteria
/ Algorithms
/ Analysis
/ Artificial Intelligence
/ Chatbots
/ Cognition & reasoning
/ College entrance achievement tests
/ Colleges & universities
/ Comparative analysis
/ Computer software industry
/ Cross-Sectional Studies
/ Dentistry
/ Education
/ Educational Measurement - methods
/ Entrance examinations
/ Equipment and supplies
/ Ethics
/ Female
/ Health occupations
/ Humans
/ International economic relations
/ Italian
/ Italy
/ Learning
/ Licensing Examinations (Professions)
/ Male
/ Medical care
/ Medical colleges
/ Medical Education
/ Meta Analysis
/ Multiple choice
/ Nursing
/ Nursing education
/ Ophthalmology
/ Organic Chemistry
/ Physical therapy
/ Quality management
/ Science education
/ Science Instruction
/ Secondary Schools
/ Speech Therapy
/ Standardized Tests
/ Students
/ Teaching
/ Test Preparation
/ Tests, problems and exercises
/ Theory of Medicine/Bioethics
/ Universities
/ Universities and colleges
2024
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Comparative accuracy of ChatGPT-4, Microsoft Copilot and Google Gemini in the Italian entrance test for healthcare sciences degrees: a cross-sectional study
by
Corradi, Federica
, Palese, Alvisa
, Rossettini, Giacomo
, Castellini, Greta
, Chiappinotto, Stefania
, Gianola, Silvia
, Turolla, Andrea
, Rodeghiero, Lia
, Cook, Chad
, Pillastrini, Paolo
in
Accuracy
/ Admission Criteria
/ Algorithms
/ Analysis
/ Artificial Intelligence
/ Chatbots
/ Cognition & reasoning
/ College entrance achievement tests
/ Colleges & universities
/ Comparative analysis
/ Computer software industry
/ Cross-Sectional Studies
/ Dentistry
/ Education
/ Educational Measurement - methods
/ Entrance examinations
/ Equipment and supplies
/ Ethics
/ Female
/ Health occupations
/ Humans
/ International economic relations
/ Italian
/ Italy
/ Learning
/ Licensing Examinations (Professions)
/ Male
/ Medical care
/ Medical colleges
/ Medical Education
/ Meta Analysis
/ Multiple choice
/ Nursing
/ Nursing education
/ Ophthalmology
/ Organic Chemistry
/ Physical therapy
/ Quality management
/ Science education
/ Science Instruction
/ Secondary Schools
/ Speech Therapy
/ Standardized Tests
/ Students
/ Teaching
/ Test Preparation
/ Tests, problems and exercises
/ Theory of Medicine/Bioethics
/ Universities
/ Universities and colleges
2024
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Comparative accuracy of ChatGPT-4, Microsoft Copilot and Google Gemini in the Italian entrance test for healthcare sciences degrees: a cross-sectional study
Journal Article
Comparative accuracy of ChatGPT-4, Microsoft Copilot and Google Gemini in the Italian entrance test for healthcare sciences degrees: a cross-sectional study
2024
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Overview
Background
Artificial intelligence (AI) chatbots are emerging educational tools for students in healthcare science. However, assessing their accuracy is essential prior to adoption in educational settings. This study aimed to assess the accuracy of predicting the correct answers from three AI chatbots (ChatGPT-4, Microsoft Copilot and Google Gemini) in the Italian entrance standardized examination test of healthcare science degrees (CINECA test). Secondarily, we assessed the narrative coherence of the AI chatbots’ responses (i.e., text output) based on three qualitative metrics: the logical rationale behind the chosen answer, the presence of information internal to the question, and presence of information external to the question.
Methods
An observational cross-sectional design was performed in September of 2023. Accuracy of the three chatbots was evaluated for the CINECA test, where questions were formatted using a multiple-choice structure with a single best answer. The outcome is binary (correct or incorrect). Chi-squared test and a post hoc analysis with Bonferroni correction assessed differences among chatbots performance in accuracy. A
p
-value of < 0.05 was considered statistically significant. A sensitivity analysis was performed, excluding answers that were not applicable (e.g., images). Narrative coherence was analyzed by absolute and relative frequencies of correct answers and errors.
Results
Overall, of the 820 CINECA multiple-choice questions inputted into all chatbots, 20 questions were not imported in ChatGPT-4 (
n
= 808) and Google Gemini (
n
= 808) due to technical limitations. We found statistically significant differences in the ChatGPT-4 vs Google Gemini and Microsoft Copilot vs Google Gemini comparisons (
p
-value < 0.001). The narrative coherence of AI chatbots revealed “Logical reasoning” as the prevalent correct answer (
n
= 622, 81.5%) and “Logical error” as the prevalent incorrect answer (
n
= 40, 88.9%).
Conclusions
Our main findings reveal that: (A) AI chatbots performed well; (B) ChatGPT-4 and Microsoft Copilot performed better than Google Gemini; and (C) their narrative coherence is primarily logical. Although AI chatbots showed promising accuracy in predicting the correct answer in the Italian entrance university standardized examination test, we encourage candidates to cautiously incorporate this new technology to supplement their learning rather than a primary resource.
Trial registration
Not required.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Analysis
/ Chatbots
/ College entrance achievement tests
/ Educational Measurement - methods
/ Ethics
/ Female
/ Humans
/ International economic relations
/ Italian
/ Italy
/ Learning
/ Licensing Examinations (Professions)
/ Male
/ Nursing
/ Students
/ Teaching
/ Tests, problems and exercises
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