Ahmed, VianBahroun, ZiedAlghazo, Mohannad2025-07-082025-07-082025-0435.232-2025.19https://hdl.handle.net/11073/26194A Master of Science thesis in Engineering Systems Management by Mohannad Alghazo entitled, “Exploring the Transformative Potential of Generative AI in Mechanical Engineering Education”, submitted in April 2025. Thesis advisor is Dr. Vian Ahmed and thesis co-advisor is Dr. Zied Bahroun. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The advent of Generative Artificial Intelligence (GAI) presents new opportunities and challenges in Mechanical Engineering Education (MEE). However, literature lacks the exploration of GAI’S application in this field. Therefore, this research highlights this gap by evaluating various free versions of GAI tools, including Code Copilot, ChatGPT/ScholarGPT, Gemini, Claude, and ChatPDF, across various aspects of the MEE curriculum. The study classifies and analyzes these tools according to their effectiveness in computational/conceptualization problems, theoretical problem-solving, image analysis & schematics, research, CAD drawing, simulation, and coding. Subsequently, a mixed exploratory research approach was deployed, incorporating qualitative and quantitative methodologies. Variables were identified through a systematic literature review and expert interviews and were then validated through surveys data analysis. Statistical techniques, including Relative Importance Index (RII), Cronbach’s α, Confirmatory Factor Analysis (CFA), and Partial Least Squares Structural Equation modeling (PLS-SEM), were conducted to identify the most significant factors and validate them, as well as to assess the relationships between enablers, challenges, strategies, psychological factors, and faculty and student perceptions of GAI. Findings suggest that Code Copilot is the most effective for computational tasks and coding related applications, while ChatGPT excels in theoretical problems, CAD drawing, and simulation, ChatPDF is particularly valuable for research, whereas Gemini and Claude demonstrate moderate effectiveness across multiple domains. PLS-SEM results confirm that enablers, challenges, and strategies influence faculty and student perceptions of GAI integration. Moreover, survey data underscores a preference for gradual GAI implementation, focusing on design, simulation, coding, and academic writing in prior to full-scale integration. Future research should expand the participant pool to include more ME faculty and students, explore advanced GAI versions, and examine direct integration of GAI tools within engineering software to enhance learning experiences.en-USGenerative AIMechanical Engineering EducationAI in EngineeringAI for AutomationNatural Language Processing (NLP) in engineeringAI in EngineeringThesis