Hannah Wu
Biographic Data
| ID | 7001233 |
|---|---|
| NAME | Hannah Wu |
| GIVEN NAMES | Hannah |
| FAMILY NAME | Wu |
| SIGNATURE | WU H |
| AFFILIATIONS | Adelaide Medical School, University of Adelaide |
| ORCID | 0000-0002-1039-9583 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
A comparison of the psychometric properties of GPT-4 versus human novice and expert authors of clinically complex MCQs in a mock examination of Australian medical students
Purpose Creating clinically complex Multiple Choice Questions (MCQs) for medical assessment can be time-consuming . Large language models such as GPT-4, a type of generative artificial intelligence (AI), are a potential MCQ design tool. Evaluating the psychometric properties of AI-generated MCQs is essential to ensuring quality.Methods A 120-item mock examination was constructed, containing 40 human-generated MCQs at novice item-writer level, 40 …
GPT-4 versus human authors in clinically complex MCQ creation: A blinded analysis of item quality
PURPOSE: To compare the structural quality of multiple choice questions (MCQs) generated by a large language model, a type of artificial intelligence (AI), GPT-4, against human-authored items at both novice and expert level. METHODS: We conducted a blinded analysis of 124 MCQs: 40 generated by GPT-4, 39 from human item-writers at Novice level, and 45 from human item-writers at Expert level. A generic prompt for GPT-4 was engineered, which include…
In vitro antibacterial activity of nimbolide against Helicobacter pylori
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In vitro antibacterial activity of nimbolide against Helicobacter pylori
GPT-4 versus human authors in clinically complex MCQ creation: A blinded analysis of item quality
PURPOSE: To compare the structural quality of multiple choice questions (MCQs) generated by a large language model, a type of artificial intelligence (AI), GPT-4, against human-authored items at both novice and expert level. METHODS: We conducted a blinded analysis of 124 MCQs: 40 generated by GPT-4, 39 from human item-writers at Novice level, and 45 from human item-writers at Expert level. A generic prompt for GPT-4 was engineered, which include…
A comparison of the psychometric properties of GPT-4 versus human novice and expert authors of clinically complex MCQs in a mock examination of Australian medical students
Purpose Creating clinically complex Multiple Choice Questions (MCQs) for medical assessment can be time-consuming . Large language models such as GPT-4, a type of generative artificial intelligence (AI), are a potential MCQ design tool. Evaluating the psychometric properties of AI-generated MCQs is essential to ensuring quality.Methods A 120-item mock examination was constructed, containing 40 human-generated MCQs at novice item-writer level, 40 …
Medicine (3 works) · Artificial Intelligence in Healthcare and Education (2 works) · Clinical Reasoning and Diagnostic Skills (2 works) · Medical education (2 works) · Psychology (2 works) · Antibacterial activity (1 works) · Antibacterial agent (1 works) · Antibiotics (1 works) · Bacteria (1 works) · Bioactive Compounds and Antitumor Agents (1 works)