Remote Laboratories or WebLabs constitute a first-order didactic resource in engineering faculties. However, in many cases, they lack a proper software design, both in the client and server side, which degrades their quality and academic usefulness. This paper presents the main characteristics of a Remote Laboratory, analyzes the software technologies to implement the client and server sides in a WebLab, and correlates these technologies with the characteristics to facilitate the selection of a technology to implement a WebLab. The results obtained suggest the adoption of a Service Oriented Laboratory Architecture-based approach for the design of future Remote Laboratories so that client-agnostic Remote Laboratories and Remote Laboratory composition are enabled. The experience with the real Remote Laboratory, WebLab-Deusto, is also presented.

Human-AI collaboration has evolved into a complex, multidimensional paradigm shaped by research in various domains. Key areas such as human-in-the-loop systems, Interactive Machine Learning (IML), Hybrid Intelligence, and Human-Agent Interaction have significantly contributed to this development. However, these fields often lack cohesion, underscoring the need for a cohesive perspective to advance. This work addresses this gap by integrating insights from diverse aspects of collaboration to present a holistic approach to fostering effective and adaptive interactions between humans and artificial agents. It emphasizes empowering end-users with greater control and involvement in decision-making processes, thereby enhancing both the levels of interactivity and adaptability within intelligent systems. Moving beyond a focus on AI training techniques, this paper presents a broader perspective on incorporating human input into AI decision-making and learning processes, highlighting the importance of flexibility in systems and user engagement. The manuscript proposes a framework encompassing five levels of human integration and examines their relationship with core collaboration aspects, including the system purpose, participant expertise, and system proactivity. By synthesizing current knowledge on human-AI collaboration and outlining essential design principles, this work aims to advance the field and foster interdisciplinary collaboration among researchers, practitioners, and designers.