Olena Bolgova
Biographic Data
| ID | 5748152 |
|---|---|
| NAME | Olena Bolgova |
| GIVEN NAMES | Olena |
| FAMILY NAME | Bolgova |
| SIGNATURE | BOLGOVA O |
| AFFILIATIONS | Alfaisal University |
| ORCID | 0009-0002-9496-9754 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Evaluating large language models as graders of medical short answer questions
The assessment of short-answer questions (SAQs) in medical education is resource-intensive, requiring significant expert time. Large Language Models (LLMs) offer potential for automating this process, but their efficacy in specialized medical education assessment remains understudied. To evaluate the capability of five LLMs to grade medical SAQs compared to expert human graders across four distinct medical disciplines. This study analyzed 804 stu…
Large language models in medical education
Large language models (LLMs) have shown promising capabilities across medical disciplines, yet their performance in basic medical sciences remains incompletely characterized. Medical histology, requiring factual knowledge and interpretative skills, provides a unique domain for evaluating AI capabilities in medical education. To evaluate and compare the performance of five current LLMs: GPT-4.1, Claude 3.7 Sonnet, Gemini 2.0 Flash, Copilot, and De…
No prominent works on this page.
Evaluating large language models as graders of medical short answer questions
The assessment of short-answer questions (SAQs) in medical education is resource-intensive, requiring significant expert time. Large Language Models (LLMs) offer potential for automating this process, but their efficacy in specialized medical education assessment remains understudied. To evaluate the capability of five LLMs to grade medical SAQs compared to expert human graders across four distinct medical disciplines. This study analyzed 804 stu…
Large language models in medical education
Large language models (LLMs) have shown promising capabilities across medical disciplines, yet their performance in basic medical sciences remains incompletely characterized. Medical histology, requiring factual knowledge and interpretative skills, provides a unique domain for evaluating AI capabilities in medical education. To evaluate and compare the performance of five current LLMs: GPT-4.1, Claude 3.7 Sonnet, Gemini 2.0 Flash, Copilot, and De…
Artificial Intelligence in Healthcare and Education (2 works) · Computer Science (2 works) · Mathematics education (2 works) · Medical education (2 works) · Medicine (2 works) · Psychology (2 works) · AI in cancer detection (1 work) · Clinical Reasoning and Diagnostic Skills (1 work) · Data science (1 work) · Innovations in Medical Education (1 work)