Maram Elzayyat
Biographic Data
| ID | 8662655 |
|---|---|
| NAME | Maram Elzayyat |
| GIVEN NAMES | Maram |
| FAMILY NAME | Elzayyat |
| SIGNATURE | ELZAYYAT M |
| AFFILIATIONS | Qatar University |
| ORCID | 0009-0004-5955-8663 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Perceived learning gains and career orientation of STEM undergraduates in the AI Era
This study examines undergraduate STEM students' perceived learning gains and career orientation within Artificial Intelligence (AI)-prevalent higher education environments in Qatar, with particular attention to engagement, satisfaction, and technological experiences. A cross-sectional, perception-based survey design was employed. A structured literature mapping process informed construct identification, followed by instrument development and ref…
Assessing LLM-generated vs. expert-created clinical anatomy MCQs
Large language models (LLMs) such as ChatGPT and Gemini are increasingly used to generate educational content in medical education, including multiple-choice questions (MCQs), but their effectiveness compared to expert-written questions remains underexplored, particularly in anatomy. We conducted a cross-sectional, mixed-methods study involving Year 2-4 medical students at Qatar University, where participants completed and evaluated three anonymi…
No prominent works on this page.
Assessing LLM-generated vs. expert-created clinical anatomy MCQs
Large language models (LLMs) such as ChatGPT and Gemini are increasingly used to generate educational content in medical education, including multiple-choice questions (MCQs), but their effectiveness compared to expert-written questions remains underexplored, particularly in anatomy. We conducted a cross-sectional, mixed-methods study involving Year 2-4 medical students at Qatar University, where participants completed and evaluated three anonymi…
Perceived learning gains and career orientation of STEM undergraduates in the AI Era
This study examines undergraduate STEM students' perceived learning gains and career orientation within Artificial Intelligence (AI)-prevalent higher education environments in Qatar, with particular attention to engagement, satisfaction, and technological experiences. A cross-sectional, perception-based survey design was employed. A structured literature mapping process informed construct identification, followed by instrument development and ref…
Perception (2 works) · AI in Service Interactions (1 work) · Anatomy and Medical Technology (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Career development (1 work) · Cognition (1 work) · Descriptive statistics (1 work) · Expatriate (1 work) · Higher education (1 work) · Innovations in Medical Education (1 work)