Harmonizing Users' and System's Requirements in Complex and Resource Intensive Application Domains by a Distributed Hybrid Approach

In an era dominated by technology, the realms of Technology-Enhanced Learning (TEL) and Cognitive Robotics stand as distinct frontiers, each characterized by its unique complexities and resource-intensive pursuits. While seemingly disparate, a closer examination reveals profound commonalities that bind these domains together. This thesis embarks on a journey to explore the intricate challenges faced by TEL and Cognitive Robotics, shedding light on their shared struggles in managing complexity, resource allocation, and performance optimization. Technology-Enhanced Learning (TEL) and Cognitive Robotics, seemingly distinct domains, actually share intricate commonalities. Both domains grapple with inherent complexities, intricately weaving pedagogy, technology, and human interaction in TEL, and the pursuit of intelligent machines capable of perceiving, reasoning, and acting in dynamic environments in Cognitive Robotics. TEL's complex interplay of education and technology introduces challenges in content delivery, engagement, and adaptability. Educators must delicately balance customization and standardization to enhance, rather than hinder, the learning process. Similarly, learners face diverse digital tools, necessitating tailored approaches to meet individual needs. In parallel, Cognitive Robotics emulates human cognitive processes, demanding advanced algorithms, resource-intensive computations, and sophisticated sensors, especially when robots collaborate with humans. Resource allocation dilemmas plague both domains, with TEL requiring judicious allocation of educational resources and Cognitive Robotics wrestling with computational, energy, and sensor allocation. The convergence of TEL and Cognitive Robotics provides a cross-disciplinary exploration opportunity. Insights from one domain may illuminate innovative solutions for analogous problems in the other. This comprehensive work delves into the nuances of each domain, addressing complexity, resource allocation, and performance optimization challenges. The Distributed Hybrid Approach presented offers a holistic analysis, guiding design and development processes. This thesis aims to contribute valuable insights to both TEL and Cognitive Robotics, fostering a symbiotic relationship between these technologically advanced realms.