The increasing emphasis on STEM (Science, Technology, Engineering, and Mathematics) education globally and the rapid development of Artificial Intelligence (AI) have highlighted the importance of equipping students with technical skills, people skills, and computational thinking. These competencies are critical for addressing the challenges of modern work environments and fostering innovative solutions. This paper presents an educational initiative that combines interactive activities, including the “Marshmallow Challenge” and Tinkercad-based coding, with the “SS Chatbot UAN,” an AI-powered tool designed to enhance learning outcomes. Conducted with secondary school students aged 14–16, this project aimed to strengthen teamwork, communication, and problem-solving skills while promoting confidence in sequential coding tasks. The findings demonstrate significant improvements in students’ collaboration and computational thinking, supported by high engagement and satisfaction levels. This study underscores the transformative potential of integrating AI technologies and experiential learning methodologies into pre-university STEM education.

The evaluation of Cyber-Physical Production Systems (CPPS) using Digital Twin (DT) architectures and Asset Administration Shells (AAS) is critical for advancing flexible, autonomous, and human-centric manufacturing environments. However, small and medium-sized enterprises (SMEs), particularly in Latin America, face significant barriers to adopting and evaluating these technologies due to resource limitations, complexity, and lack of standardized frameworks. This paper proposes a scalable and modular evaluation framework specifically designed for SMEs, integrating DTs for real-time simulation and monitoring, AAS for semantic interoperability, and simulation-based assessment methods to measure CPPS autonomy, flexibility, and resilience. A comprehensive review of recent approaches, trends, challenges, and research gaps highlights the urgent need for dynamic, standardized, and cognitive-enabled evaluation methodologies. The proposed framework addresses these needs by formalizing production knowledge through ontologies, synchronizing physical systems with digital models, and enabling systematic performance evaluation across diverse production scenarios. A case study conducted in a Latin American manufacturing SME demonstrates the practical application of the framework, showing improvements in production efficiency, decision transparency, and system adaptability. The results underline the framework’s potential to democratize access to advanced Industry 4.0 solutions for SMEs, fostering more resilient and intelligent manufacturing ecosystems. By bridging current technological gaps, this work contributes to accelerating the digital transformation of SMEs and supports the broader goals of Industry 5.0. Future research will explore the integration of explainable AI techniques and expand the framework’s applicability to multi-site production networks.

“Artificial Intelligence in Industry 4.0”, a technical report published by the working groups "Technological and Application Scenarios" and "Artificial Intelligence" (AI) of the Industry 4.0 (I4.0) platform, presents an innovative Industrial AI concept. Above all, it concludes that I4.0 experts and scientists must become accustomed to the behavior of autonomous AI-controlled systems, collaborate with them and comply with learnability requirements (predictability). Industrial AI instantly raises a set of concerns about existing norms and new standardizations. These frequently provide guidelines and, in some cases, offer procedures and implementations using design patterns. One way to produce AI in I4.0 systems is through Industrial Agents (IAs) due to their natural autonomy and additional intelligent characteristics, e.g., reactiveness, proactiveness, and human cooperativeness. Multi-Agent Systems (MASs) are particularly well suited for representing distributable AI that can develop I4.0 components being applied to various I4.0 scenarios. Considering the properties of IAs and the corresponding standards, an MAS architecture is used to understand the aspects of the flexible, intelligent, and automated Cyber-Physical Production System (CPPS). This article proposes a predictive IA for I4.0 (Agent4.0) to an agent-based CPPS architecture, leveraging IA design patterns and logical structure for implementing MAS. As a result, relevant standardized IA design patterns for I4.0 show how MAS can be created with the help of the Industrial AI requirements and Agent4.0 skills (functions) identified.

The training of Deep Reinforcement Learning algorithms on robotic devices is challenging due to their large number of actuators and limited number of feasible action sequences. This paper addresses this challenge by extending and transferring existing approaches for waypoint-based exploration with Hierarchical Reinforcement Learning to the domain of robotic devices. The resulting algorithm utilizes a top-level policy, which suggests waypoints to a bottom-level policy that controls the system actuators. The waypoints can either be provided to the top-level policy as domain knowledge or be learned from scratch. The algorithm explicitly accounts for the low number of feasible waypoints and waypoint transitions that are characteristic of robotic devices. The effectiveness of the approach is evaluated on the simulation of a research demonstrator, and a separate ablation study proves the importance of its components.

In recent years, new control automation schemes resulted from the complex needs of the manufacturing systems. The dynamism of the current market is not covered by traditional hierarchical structures yet. Thus, innovative models facing the demands of industrial requirements are now based on intelligent entities. These modern paradigms are realized by evolved programming techniques to implement flexible and distributed control systems (non-hierarchical), e.g. a Cyber-Physical System (CPS). CPS is described as an innovative model that involves transdisciplinary engineering. In fact, CPS defines a modern term that integrates additional information (e.g. real-time data) and modern communication technology (ICT) into the physical world. CPS is often associated with the Industry 4.0 (I4.0) perspective and its specific implementation could be defined by holons, a software built by autonomous and intelligent entities. A holon present in a CPS may contain a physical resource that is linked to the software system by distributable and suitable communication protocols (e.g. based on the IEC 61499 standard). The results of this paper show that both paradigms-holonic and CPS-are a complementary way for manufacturing control automation. The goal of this research is sustained by an overview of UML models, and the application of industrial Ethernet-based protocol (Profinet), to satisfy a distributed CPS with real-time characteristics.

Multi-Agent Systems (MAS) are an implementation paradigm frequently discussed to be used within control system design for flexible production systems. Over the years, on the one hand different kind of MAS-based control architectures, and on the other hand, different agent system design methodologies have been developed with different intentions and focus e.g. Agent-Oriented Software Engineering (AOSE). Nevertheless, only a few practical applications of MAS in control can be observed. In fact, a MAS developers' problem is the missed availability of design support for agent-based engineering, to control complex production systems. Therefore, this paper intends to highlight a path towards such a MAS development support exploiting design patterns.

Cyber-Physical Systems (CPS) could be the most modern electronic development as yet, thanks to the integration of information and communication technology (ICT). CPS has associated with computer systems (cyber part) which are closely related to the real-world processes (physical part). A CPS is supported by the newest and foreseeable further advances of computer science, data and communication equipment on the one hand, and of manufacturing science and tools, on the other. On the contrary, within the multiple applications, there are CPS for manufacturing systems, called CPPS (Cyber-Physical Production Systems). The fourth industrial revolution regularly distinguished as I4.0 is based on CPPS. Considerable numbers of authors agree that paradigms agent-based as Multi-Agent Systems (MAS) converge or they make up some parts to apply CPPS. In general, this paper emphasizes that there are different important approaches in CPPS implementation which point near, in particular to MAS. The objective of this article is to provide general and specific concepts associated CPPS implementation through agents, considering the current multiples approaches and methods. A key result is that Agent-Oriented Software Engineering (AOSE) methodologies have been a highlight to comparisons leading benefits to apply in CPPS.