In contemporary education, traditional methodologies often struggle to captivate today's learners, who are deeply immersed in a fast-paced, tech-driven world. To effectively engage this generation, it's imperative to adopt innovative teaching methods that cater to diverse learning styles and leverage cutting-edge technologies. This challenge is particularly pertinent in developing an emotion recognition system capable of analyzing raw images or facial landmarks to discern the emotions exhibited by individuals.

Our Educational Innovation Project, "DEMOSEI - Design and implementation of demonstrator systems for their application in the teaching-learning process in Intelligent Electronic Systems subjects," aims to pioneer a novel approach in integrating emotion recognition technology into educational settings. This work focuses on utilizing deep learning models, such as Convolutional Neural Networks (CNNs) and facial landmark detection algorithms, to accurately identify and interpret human emotions. By harnessing the power of these artificial intelligence-driven tools, students can explore and understand the intricacies of emotional cues through practical applications.

The project encompasses a comprehensive pipeline from data acquisition and preprocessing to model training and deployment, empowering students to delve into every stage of artificial intelligence development. The system is designed as an open platform, encouraging students to experiment with different deep learning architectures and customization options. Leveraging TensorFlow and Keras in Python, the system supports deployment on versatile platforms like Google Colab or Raspberry Pi, ensuring accessibility and scalability in educational environments.

The system holds significant relevance across diverse academic disciplines, including computer science, psychology, and artificial intelligence. Its interdisciplinary nature not only enhances educational experiences but also fosters collaboration and innovation among students from varied backgrounds. By bridging the gap between theoretical knowledge and practical application, this system prepares learners to navigate the evolving landscape of artificial intelligence and contribute meaningfully to the field.

Keywords: Deep learning, emotion recognition, challenge-based learning methodology, student motivation.