Interdisciplinary DSP and AI Integration

This paper demonstrates how digital signal processing (DSP) and artificial intelligence (AI) together form a unifying platform across diverse applications and educational contexts. Through case studies in biomedicine, rehabilitation, sport activities, and renewable energy, students are encouraged to collect their own datasets, explore the mathematical foundations of signal processing, and validate results through practical implementations. The rapid development of AI is reshaping not only technological innovation but also education, as students increasingly prefer interactive resources that support independent exploration while teachers adopt new roles that integrate guidance with hands-on, AI-driven methods. By working with wearable and mobile sensors, students gain practical insights into DSP and AI applications, supported by communication systems, remote data processing, and advanced visualization. DSP provides a common mathematical framework, while AI extends its scope to robotics, digital twins, neurology, augmented reality, and energy optimization. Integrating AI with DSP, supported by innovative teaching strategies, enhances both theoretical understanding and practical problem solving, preparing students for interdisciplinary research and professional practice.