Nonverbal communication is essential for effective oral presentations, shaping audience engagement and conveying confidence beyond spoken words. Multimodal Learning Analytics (MmLA) research has advanced the automatic detection of presenters’ behaviors—such as posture, gestures, and eye contact—but continues to explore how to provide actionable feedback that fosters reflection and skill development. Recent advances in Generative Artificial Intelligence (GenAI) offer new opportunities to automate the analysis of nonverbal cues and generate narrative evaluations that go beyond raw metrics. This study investigates the integration of a Learning Analytics Dashboard (LAD) with a Large Language Model (LLM) to deliver both numerical and narrative feedback across five dimensions of nonverbal communication: body posture, eye contact, hand gestures, facial expression, and use of space. Twenty-two undergraduate students participated in the study, receiving numerical feedback through a LAD and narrative feedback from either a human expert or an LLM. Findings revealed no significant differences in students’ perceptions of human- and LLM-generated feedback, while also highlighting the potential of LLM-based feedback to support reflection despite certain technical limitations. These results suggest that LLMs can meaningfully enhance learning analytics dashboards by delivering actionable, human-like feedback that supports the development of nonverbal communication skills.

Modern manufacturing processes’ increasing complexity and variability demand advanced systems capable of real-time monitoring, adaptability, and data-driven decision-making. This paper introduces a novel runtime condition model to enhance interoperability, data integration, and decision support within intelligent manufacturing environments. The model encapsulates key manufacturing elements, including asset management, relationships, key performance indicators (KPIs), capabilities, data structures, constraints, and configurations. A key innovation is the integration of a knowledge graph enriched with embedding techniques, enabling the inference of missing relationships, dynamic reasoning, and predictive analytics. The proposed model was validated through a case study conducted in collaboration with TQC Automation Ltd., using their MicroApplication Leak Test System (MALT). A dataset of over 9,000 unique test configurations demonstrated the model’s capabilities in representing runtime conditions, managing operational parameters, and optimising test configurations. The enriched knowledge graph facilitated advanced analyses, providing actionable insights into test outcomes and enabling proactive decision-making. Empirical results showcase the model’s ability to harmonise diverse data sources, infer missing connections, and improve runtime adaptability. This study highlights the potential of combining runtime modelling with knowledge graphs to address the challenges of modern manufacturing. Future research will explore the model’s application to additional domains, integration with larger datasets, and the use of machine learning for enhanced predictive capabilities.

Abstract Smart Product–Service Systems (S-PSS) represent an innovative business model that integrates intelligent products with advanced digital capabilities and corresponding e-services. The user experience (UX) within an S-PSS is heavily influenced by the customization of services and customer empowerment. However, conventional UX analysis primarily focuses on the design stage and may not adequately respond to the evolving user needs during the usage stage and how to exploit the data surrounding the use of S-PSS. To overcome these limitations, this article introduces a practical framework for developing Adaptive User Interfaces within S-PSS. This framework integrates ontologies and Context-aware recommendation systems, with user interactions serving as the primary data source, facilitating the development of adaptive user interfaces. One of the main contributions of this work lies on the integration of various components to achieve the creation of Adaptive User Interfaces for digital services. A case study of a smart device app is presented, to demonstrate the practical implementation of the framework, with a hands-on development approach, considering technological aspects and utilizing appropriate tools. The results of the evaluation of the recommendation engine show that using a context-aware approach improves the precision of recommendations. Furthermore, pragmatic aspects of UX, such as usefulness and system efficiency, are evaluated with participants with an overall positive impact on the use of the smart device.

Smart-product service systems are a business strategy that combines product and service into one value proposition. The user experience of digital services and the smart product can be a clear differentiator among competitors to achieve economically sustainable solutions. Hence, offering a more personalized experience is an important aspect of S-PSS. This paper aims to provide a theoretical framework for a context-aware user experience in S-PSS by providing adaptive and personalized services to the users according to their needs in a given context, by exploiting the digital capabilities of smart products and referring to the use of recommendation systems. The paper presents an application scenario using a smart-wearable as an example of a product-oriented PSS to better describe the framework and each component while stating the future challenges.