Bridging AI, Robotics, and Software Engineering: An Interdisciplinary Approach for Learning Emerging Technologies

As the rapid evolution of emerging technologies continues to reshape industries, the need for an interdisciplinary approach to learning has become critical. Artificial intelligence (AI), robotics, and software engineering are key fields driving innovation, yet they are often taught in isolation. This paper advocates for a comprehensive, interdisciplinary learning model that integrates AI, robotics, and software engineering to prepare students and professionals for the demands of the future workforce. By blending theoretical knowledge with handson experience across these domains, the proposed approach fosters a deeper understanding of how these technologies intersect and support each other in real-world applications. The main problem addressed in this article is the fragmentation of higher education curricula, where AI, robotics, and software engineering are traditionally taught as separate subjects. This separation limits students' ability to grasp how these fields can collaborate to solve complex problems, leading to a knowledge gap that hinders their ability to innovate in an interconnected technological landscape. The proposed interdisciplinary model aims to close this gap by providing a holistic education that integrates key principles from all three domains. This approach aims to equip students with a versatile skill set that is adaptable to the rapidly changing technological environment. Through a combination of lectures, lab work, and project-based learning, students will explore the synergies between AI algorithms, robotic systems, and software development processes. The curriculum emphasizes the importance of collaboration between these fields, demonstrating how AI can enhance robotic autonomy, how software engineering principles ensure the scalability of AI models, and how robotics offers practical applications for these technologies. The reasoning behind this interdisciplinary model is grounded in the idea that emerging technologies do not exist in silos. For example, advancements in AI are closely related to robotics, where intelligent systems enhance machine learning in autonomous systems. Similarly, software engineering provides the framework for the deployment of these AI-driven robotic solutions on a scale. By teaching students to think across these disciplines, we can cultivate a new generation of technologists capable of addressing the multifaceted challenges posed by emerging technologies. In conclusion, the proposed approach seeks to bridge the gap between AI, robotics, and software engineering by fostering an integrated learning experience. This model not only prepares students for the technical demands of future careers, but also promotes innovation by encouraging the cross-pollination of ideas across disciplines. By embracing this holistic approach, we can better prepare the next generation to contribute meaningfully to the evolving landscape of technology.