Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity

A Master of Science thesis in Engineering Systems Management by Humaid Abdalla Al Naqbi entitled, “Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity”, submitted in August 2024. Thesis advisor is Dr. Zied Bahroun and thesis co...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: AlNaqbi, Humaid Abdalla (author)
التنسيق: doctoralThesis
منشور في: 2024
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/11073/25640
الوسوم: إضافة وسم
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author AlNaqbi, Humaid Abdalla
author_facet AlNaqbi, Humaid Abdalla
author_role author
dc.contributor.none.fl_str_mv Bahroun, Zied
Ahmed, Vian
dc.creator.none.fl_str_mv AlNaqbi, Humaid Abdalla
dc.date.none.fl_str_mv 2024-09-30T09:42:08Z
2024-09-30T09:42:08Z
2024-08
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv 35.232-2024.40
https://hdl.handle.net/11073/25640
dc.language.none.fl_str_mv en_US
dc.subject.none.fl_str_mv Artificial intelligence
Generative artificial intelligence
Productivity
Organizational workplace
Education
Educational technology
ChatGPT
Ethics
Management
Chatbots
Knowledge management
dc.title.none.fl_str_mv Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/doctoralThesis
description A Master of Science thesis in Engineering Systems Management by Humaid Abdalla Al Naqbi entitled, “Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity”, submitted in August 2024. Thesis advisor is Dr. Zied Bahroun and thesis co-advisor is Dr. Vian Ahmed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
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spelling Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee ProductivityAlNaqbi, Humaid AbdallaArtificial intelligenceGenerative artificial intelligenceProductivityOrganizational workplaceEducationEducational technologyChatGPTEthicsManagementChatbotsKnowledge managementA Master of Science thesis in Engineering Systems Management by Humaid Abdalla Al Naqbi entitled, “Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity”, submitted in August 2024. Thesis advisor is Dr. Zied Bahroun and thesis co-advisor is Dr. Vian Ahmed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Over the last several decades, the globe has seen remarkable growth in science and technology, which has resulted in fundamental advancements in a variety of areas and disciplines. This growth highlights the importance of artificial intelligence in human history, as it opens new horizons for leveraging advanced programs and technologies to enhance and increase organizational performance in a sustainable manner. However, despite this significant progress, there is still a research gap in the applications of Generative AI (GAI) in engineering and academic disciplines, as the challenges and opportunities associated with these fields have not been adequately studied. This study aims to fill this gap by investigating how GAI applications can be integrated to enhance productivity among students and faculty in the academic and engineering disciplines, which are vital sectors for the development of technological innovations. The research also addresses how to adopt this technology in a responsible and ethical manner, especially in these two important sectors. The study also included interviews and semi-structured surveys with faculty and students at a prestigious institution to explore their experiences, attitudes, and expectations regarding the use of Generative AI. Analyzing the data using the Relative Importance Index (RII) method, the results showed that compliance standards to mitigate bias were a top concern among faculty members, a point that was also confirmed by students. This study provides an important basis for future research aimed at guiding educational institutions towards effective and sustainable implementation of this technology.College of EngineeringMultidisciplinary ProgramsMaster of Science in Engineering Systems Management (MSESM)Bahroun, ZiedAhmed, Vian2024-09-30T09:42:08Z2024-09-30T09:42:08Z2024-08info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2024.40https://hdl.handle.net/11073/25640en_USoai:repository.aus.edu:11073/256402025-06-26T12:23:48Z
spellingShingle Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity
AlNaqbi, Humaid Abdalla
Artificial intelligence
Generative artificial intelligence
Productivity
Organizational workplace
Education
Educational technology
ChatGPT
Ethics
Management
Chatbots
Knowledge management
status_str publishedVersion
title Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity
title_full Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity
title_fullStr Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity
title_full_unstemmed Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity
title_short Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity
title_sort Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity
topic Artificial intelligence
Generative artificial intelligence
Productivity
Organizational workplace
Education
Educational technology
ChatGPT
Ethics
Management
Chatbots
Knowledge management
url https://hdl.handle.net/11073/25640