Industry 5.0 is emerging across sectors like medicine, fishing, mining, research, automation, and education, emphasizing human-technology collaboration for greater efficiency, innovation, and sustainability. Unlike Industry 4.0, which prioritized automation, Industry 5.0 reintegrates the human element to foster synergistic systems between humans and machines. In automation and education, technologies like collaborative robots, AI, and IoT are increasingly included in university curricula to prepare students for this new era. This paper aims to develop an Industry 5.0 teaching system using three educational modules: a collaborative robot arm, a conveyor belt with AI-driven quality control, and an IoT module for data collection and cloud analysis. These modules assess students' prior knowledge and learning outcomes. Implemented as final lab exercises in the Industrial Intelligent Automation B course at Pontificia Universidad Católica del Perú, these modules targeted 7th-semester students and beyond. They allowed students to engage with real-world industrial systems, deepening their understanding of Industry$\mathbf{5. 0}$principles. The results showed significant improvements in students' theoretical and practical knowledge, as reflected in their final grades. This hands-on approach effectively combined advanced and conventional technologies to enhance technical skills and real-world application abilities.

This innovative practice paper describes the design, development and validation of PlatROB, an educational platform for teaching robotics and artificial intelligence (AI). The increasing demand for advanced robotic systems necessitates that engineering students enhance their system integration skills specific to robotics and AI, which requires specialized hardware and software for effective learning. PlatROB is a low-cost, modular robotic platform that fosters the development of robotic system integration abilities across key engineering disciplines including mechanics, electronics, and programming. Using Pla-tROB, students can perform different basic mobile movement types, such as Ackermann, differential and omnidirectional. The modular design allow students to experiment with each movement type by simply adding specific modules for control and processing, sensing, and actuation. Furthermore, the structure and mechanisms of PlatROB are 3D printable, familiarizing students with rapid prototyping technologies. Additionally, the platform is capable of being programmed with teleoperation algorithms and autonomous navigation using computer vision. To support the learning process, detailed manuals and test codes were provided to facilitate assembly and verify proper module integration. The learning effectiveness of PlatROB was evaluated in workshops with mechatronics engineering students from different program levels. A mixed-methods approach was utilized, combining quantitative (pre- and post-questionnaires) and qualitative (observation charts and surveys) tools. The results highlighted PlatROB's versatility as an educational tool for undergraduate students, enhancing their understanding of robotics systems integration. Early-stage students gained foundational knowledge in ground vehicle configurations, robotics technology, testing, and programming, while advanced students reinforced their understanding of complex robotics and AI concepts and implementation, including computer vision. The modular design enabled customized learning pathways, increasing students' technical skills and system-thinking abilities through hands-on, collaborative projects.

This full paper presents the validation and results of a low cost scaled car platform into a project-based course in order to teach AI self-driving cars topics for undergraduate programs in Universities. This is an elective course of the Mechatronics program at Pontificia Universidad Catolica del Peru (PUCP) whose second edition of the course was developed during January through March 2020. The main objective of this article is to present the results of the second edition of the project-based course, which details the integration of a low cost robotic platform with an embedded board used to execute computer vision and AI algorithms. Using a robotic platform allowed the students to focus on the application of the algorithms in a real scenario and learn from experience instead of using only simulation platforms. The proposed course aims to introduce the students in self-driving cars topics, and apply the theoretical concepts to develop an autonomous car using the robotic platform. The topics of the course are structured in five categories including Automotive Design Concepts, Localization and Navigation, Computer Vision Techniques, Artificial Intelligent Techniques and Simulation Environment; and is divided into fourteen theoretical lectures and five practical laboratories. The project-based course is aligned with four Students Outcomes from ABET accreditation entity for undergraduate programs in order to reinforce their abilities to work as a team, self-learning, hands-on experience, develop prototypes, testing in real scenarios, and learn basic scientific writing and presentation skills. The results of the second edition of the course show that the students enrolled were able to accomplish the development of a self-driving car capable of completing a lap on a racetrack autonomously only using image processing and AI algorithms. In comparison with the first edition of the course, the inclusion of a scaled car as a base for the project avoided mechanical problems with the chassis and allowed the students to focus on the sensors integration and algorithms programming.