Industry 5.0 emphasises social sustainability and highlights the critical need for personnel upskilling and reskilling to achieve the seamless integration of human expertise and advanced technology. This paper presents a methodological framework for designing personalised training programs that support personnel upskilling, with the goal of fostering flexibility and resilience amid rapid changes in the industrial landscape. The proposed framework encompasses seven stages: (1) Integration with Existing Systems, (2) Data Collection, (3) Data Preparation, (4) Skills-Models Extraction, (5) Assessment of Skills and Qualifications, (6) Recommendations for Training Program, (7) Evaluation and Continuous Improvement. By leveraging Large Language Models (LLMs) and human-centric principles, our methodology enables the creation of tailored training programs to help organisations promote a culture of proactive learning. This work thus contributes to the sustainable development of the human workforce, facilitating access to high-quality training and fostering personnel well-being and satisfaction. Through a food-processing use case, this paper demonstrates how this methodology can help organisations identify skill gaps and upskilling opportunities and use these insights to drive personnel upskilling in Industry 5.0.

With its various available frameworks and possible devices, augmented reality is a proven useful tool in various industrial processes such as maintenance, repairing, training, reconfiguration, and even monitoring tasks of production lines in large factories. Despite its advantages, augmented reality still does not usually give meaning to the elements it complements, staying in a physical or geometric layer of its environment and without providing information that may be of great interest to industrial operators in carrying out their work. An expert’s remote human assistance is becoming an exciting complement in these environments, but this is expensive or even impossible in many cases. This paper shows how a machine learning semantic layer can complement augmented reality solutions in the industry by providing an intelligent layer, sometimes even beyond some expert’s skills. This layer, using state-of-the-art models, can provide visual validation and new inputs, natural language interaction, and automatic anomaly detection. All this new level of semantic context can be integrated into almost any current augmented reality system, improving the operator’s job with additional contextual information, new multimodal interaction and validation, increasing their work comfort, operational times, and security.

Generative AI presents a profound challenge to traditional notions of human uniqueness, particularly in creativity. Fueled by neural network based foundation models, these systems demonstrate remarkable content generation capabilities, sparking intense debates about authorship, copyright, and intelligence itself. This paper argues that generative AI represents an alternative form of intelligence and creativity, operating through mathematical pattern synthesis rather than biological understanding or verbatim replication. The fundamental differences between artificial and biological neural networks reveal AI learning as primarily statistical pattern extraction from vast datasets crystallized forms of collective human knowledge scraped from the internet. This perspective complicates copyright theft narratives and highlights practical challenges in attributing AI outputs to individual sources. Rather than pursuing potentially futile legal restrictions, we advocate for human AI synergy. By embracing generative AI as a complementary tool alongside human intuition, context, and ethical judgment, society can unlock unprecedented innovation, democratize creative expression, and address complex challenges. This collaborative approach, grounded in realistic understanding of AIs capabilities and limitations, offers the most promising path forward. Additionally, recognizing these models as products of collective human knowledge raises ethical questions about accessibility ensuring equitable access to these tools could prevent widening societal divides and leverage their full potential for collective benefit.

Older adults often need guidance when visiting new buildings for the first time. However, indoor navigation remains challenging due to the lack of Global Positioning System (GPS) availability, visually repetitive corridors, and frequent location failures. This article presents a multimodal indoor navigation assistant that combines graph-based route planning with visual landmark verification to provide step-by-step guidance. The environment is modelled as a directed graph whose nodes are annotated with semantic landmarks, and the graph is constructed primarily from a video of the building, reducing the need for 3D scanners, beacons, or other specialised instruments. Routes are calculated using Dijkstra’s shortest-path algorithm over the semantic graph. During navigation, camera frames are analysed using a restricted vision-language recognition strategy that only considers candidate landmarks from the current and next nodes, reducing false detections and improving interpretability. To increase robustness, a temporary voting mechanism was introduced to confirm node transitions, as well as a hierarchical redirection strategy with local and global recovery. The system is implemented in two modes: handheld mode with visual cues using augmented reality arrows, mini map and voice instructions, and hands-free mode with front camera using voice instructions and keywords. Evaluation involved preliminary technical testing in the United Kingdom followed by formal user validation in Spain. During these trials, participants reported high usability, strong confidence and safety, and increased perceived independence.

Traditionally, computer graphics courses have centered on desktop environments using well-known frameworks such as OpenGL. That approach might become obsolete in a new context in which developers must create applications for smart phones, tablets, and rich Internet applications. Teaching computer graphics in this situation is becoming difficult; traditional tools don't address these new requirements in an integrated way. To deal with this situation, the Processing development environment provides high-end solutions in visualization, animation, and interaction, while letting students deploy their programs on desktop computers, smart phones, tablets, and websites. Instructors can introduce devices and interaction paradigms in just a few hours. In an introductory computer graphics course, Processing dramatically boosted students' motivation. Their work wasn't just visible in the lab; they could show it to classmates and friends on their own smart phones, tablets, and websites.