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Maram Elzayyat

Biographic Data

ID8662655
NAMEMaram Elzayyat
GIVEN NAMESMaram
FAMILY NAMEElzayyat
SIGNATUREELZAYYAT M
AFFILIATIONSQatar University
ORCID0009-0004-5955-8663
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
WORKS AS AUTHOR2
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2025
LATEST PUBLICATION YEAR2026
H-INDEX0
  • Perceived learning gains and career orientation of STEM undergraduates in the AI Era

    Open Access•Sumaiya Muneer, Maram Elzayyat et al.•ARTICLE•Social Sciences & Humanities Open•2026

    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

    Open Access•Maram Elzayyat, Mohammad Javed Ansari et al.•ARTICLE•Medical Education Online•2025

    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…

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  • Assessing LLM-generated vs. expert-created clinical anatomy MCQs

    Open Access•Maram Elzayyat, Mohammad Javed Ansari et al.•ARTICLE•Medical Education Online•2025

    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

    Open Access•Sumaiya Muneer, Maram Elzayyat et al.•ARTICLE•Social Sciences & Humanities Open•2026

    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)

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