Generative AI Integration: Key Drivers and Factors Enhancing Productivity of Engineering Faculty and Students for Sustainable Education

Generative Artificial Intelligence (GAI) technologies are revolutionizing productivity and creativity across educational and engineering contexts. This study addresses a critical gap by examining the key factors influencing the successful integration of GAI tools to enhance faculty and student productivity, with a focus on higher education and its role in advancing sustainable development. Specifically, it investigates challenges, opportunities, and essential conditions for effective GAI adoption that support not only academic excellence but also the preparation of engineers capable of addressing global sustainability challenges in line with the United Nations Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production). A preliminary literature review identified significant factors requiring attention, further refined through interviews with 14 students and 13 faculty members, and expanded upon via a survey involving 54 students and 42 faculty members. Participants rated the significance of various factors on a five-point Likert scale, allowing for the calculation of the Relative Importance Index (RII). The findings reveal that while compliance with ethical standards and bias mitigation emerged as the most significant concerns, mid-level considerations such as institutional support, training, and explainability are critical for fostering GAI adoption in sustainable learning environments. Foundational elements, including robust technical infrastructure, data security, and scalability, are vital for long-term success and alignment with responsible and sustainable innovation. Notably, this study highlights a divergence in perspectives between faculty and students regarding GAI’s impact on productivity, with faculty emphasizing ethical considerations and students focusing on efficiency gains. This study offers a comprehensive set of considerations and insights for guiding GAI integration in educational and engineering settings. It emphasizes the need for multidisciplinary collaboration, continuous training, and strong governance to balance innovation, responsibility, and sustainability. The findings advance theoretical understanding and provide practical insights for academia, policymakers, and technology developers aiming to harness GAI’s full potential in fostering sustainable engineering education and development.