Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs’ travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.

“You are imparting human qualities to it because it looks human—but I assure you: it is not. If it were a box on wheels, I would not be facing this opposition.” Commander Bruce Maddox spoke these words during a Star Trek, The Next Generation episode as Lieutenant Commander Data faced an enquiry into his sentience. Similarly, this literature review explores the role of generative artificial intelligence (GenAI) in education, specifically examining its function as a learning assistant within the Information and Communication Technology (ICT) discipline in higher education. Using AI‐powered tools such as Litmaps and Google Scholar, we conducted an iterative search and reviewed 26 peer‐reviewed articles, focusing on the use of GenAI tools as learning assistants in ICT education. We adhered to predefined inclusion and exclusion criteria to ensure methodological rigour, excluding non‐ICT populations, secondary sources, and non‐peer‐reviewed literature. The findings highlight the manifold advantages of integrating GenAI into ICT education, including its ability to diagnose programming code, provide tailored feedback, and serve as a valuable learning aid. However, significant challenges were also identified, particularly the tendency of novice programmers to uncritically accept GenAI‐generated output, concerns over academic integrity violations, and issues surrounding the privacy of personal information. We pinpoint that the integration of GenAI influences four key components: Andragogy, Assessment, Integrity, and Privacy. As GenAI becomes more entrenched in educational practices, this research provides a foundation for further exploration and refinement to maximise its transformative potential in higher education.