Artificial intelligence (AI) has been recognized by the World Health Organization for its transformative potential in addressing global reproductive healthcare challenges, including inequitable access to monitoring and treatment and limited diagnostic precision. AI offers significant promise in enhancing diagnostic accuracy enabling data-driven decision-making for personalized preventive and therapeutic interventions. However, its deployment also raises ethical and operational concerns, such as data privacy risks, algorithmic bias, legal complexities, cultural sensitivity, overreliance on AI-generated recommendations, and the potential deskilling of clinicians. Addressing these challenges requires inclusive frameworks for responsible integration. Moreover, AI-driven digital transformation must align with the broader call for sustainable innovation outlined in the United Nations’ 2030 Agenda and European Union regulations defining the requirement for safe and secure integration standards. This research explores pathways for responsible and sustainable AI adoption in reproductive healthcare while mitigating associated risks. It introduces the (H)iCARE framework, which advocates for (1) human-centric hybrid models that integrate AI-driven innovations with clinical expertise to ensure balanced innovations. The framework also (2) embraces a broader, humanity-oriented perspective to ensure inclusivity beyond a limited subset of stakeholders, and (3) fosters a learning-driven approach that prioritizes continuous skill development to prevent cognitive complacency. While developed in the context of reproductive healthcare, its principles extend across the healthcare sector, providing a foundation for AI-integrated information system design and a roadmap for ethical, sustainable advancements, additionally fostering discussion on future research priorities.

Background In today's world, grappling with the dual challenges of energy scarcity and climate change, the agricultural and food supply chains are at a crucial juncture for transformation. Data privacy within these sectors is increasingly significant, as the integration of advanced digital platforms becomes essential for improving efficiency and sustainability. Scope and Approach This systematic literature review (SLR) explores the development and application of privacy-preserving data platforms specifically tailored to the agricultural and food supply chains. The review focuses on the evolving landscape of data architectures, data sovereignty, and advanced privacy-preserving techniques. Techniques such as anonymization, encryption, differential privacy, and federated learning are examined, along with the legal and ethical considerations surrounding data sharing in the context of global energy and climate-related challenges. Key Findings and Conclusions The review synthesizes findings from a broad spectrum of studies published over the last decade, uncovering significant advancements in privacy-preserving technologies which may show their benefit for the agricultural and food sectors. It identifies dominant research trends and promising future directions for enhancing data security and sustainability. The key findings underscore the vital importance of safeguarding data privacy in these sectors, highlighting the potential of advanced privacy techniques to protect sensitive agricultural data while balancing the need for transparency and operational effectiveness. The review concludes that adopting these innovative privacy methods is crucial for fostering a more sustainable and secure future for agriculture and food systems, contributing to the global Sustainable Development Goals such as SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production) by improving precision agriculture, optimizing yields (crop and animal) and reducing food waste.

Smart food systems generate vast and diverse data across the supply chain, yet inconsistent data structures and limited interoperability hinder their full potential. Achieving semantic interoperability, where systems can exchange and interpret data with shared meaning, is essential for enabling intelligent integration and decision-making. Tools such as ontologies, knowledge graphs, and reasoning engines play a key role in this process. In this paper, we refer to these as Semantic Interoperability (SI) tools: a broad category that includes technologies grounded in Semantic Web standards (e.g., RDF, OWL, SPARQL) but emphasizes their applied role in aligning meaning across heterogeneous systems. Coupled with eXplainable Artificial Intelligence (XAI), these technologies enhance transparency and trust in AI-driven decisions, such as personalized food recommendations tailored to an individual’s health conditions and preferences. This paper presents a Systematic Literature Review (SLR) examining the role of semantic interoperability tools and XAI in the development of smart food systems. Through an analysis of 39 studies, the review identifies key semantic technologies and XAI methods used in food systems, with a focus on their application in intelligent food recommendation systems. The findings reveal that while significant progress has been made, current systems often lack adequate transparency and personalization, limiting user trust and engagement. To address these gaps, the paper proposes the integration of semantic interoperability tools with XAI to create smarter, more reliable food systems. As part of this effort, the paper introduces the conceptual model for the Semantic Explainable Food Recommendation Ontology (SEFRO), a work-in-progress ontology, designed to connect entities and relationships within food systems in an intelligent manner, with the goal of enabling personalized, explainable, and interoperable food recommendations that meet the growing demands for smart food systems.

The increasing demand for software solutions in the coming years will surpass the availability of IT talent, driving interest in citizen development and low-code approaches. However, the lack of technical insight among citizen developers poses potential security risks. This research aims to support businesses adopting citizen development by providing a framework that helps to proactively identify security risks by also linking them to specific actors and tools needed during the system design and development process to mitigate those risks. Additionally, this framework helps to address knowledge gaps by outlining actionable steps to ensure secure low-code development practices. The research aims to answer the question: "How can contextual information be modeled in low-code platforms to proactively identify and address security-related issues, acting as a virtual mentor for citizen / low-code developers?". To answer this question, our research conceptualizes security risks from established frameworks and operational security methodologies into a practical framework that allows mapping security risks to the context of low-code development. This framework serves as a foundational platform for designing and integrating active process-oriented guidance within low-code platforms using model-based automated prompts. This approach additionally aligns with DevSecOps principles that allows enhancing the capacity for low-code approach and citizen development in areas that currently may include manual coding and integrations.

AI-based conversational agents hold significant promise for transforming educational processes, yet there is a lack of empirical research examining users' perceptions of their benefits, concerns, and the implications for design. This study investigates these perceptions among graduate and under-graduate students and educators at the University of Twente (UTwente) in the Netherlands, focusing on the potential advantages and challenges of integrating AI chatbots into education. The study aims to provide preliminary insights into design considerations for implementing AI-driven chatbots as a "buddy system" to support student learning for enhanced learning process and outcome quality. The pilot study involved 58 participants, including bachelor's and master's students from various disciplines, PhD researchers, and educators. The findings contribute to the co-creation of an AI-based StudyBuddy tool, designed to assist students in their academic journey.

Prior research indicates that chatbots have the capacity to significantly enhance learning performance, student satisfaction, and engagement. Chatbots are employed in various educational contexts, serving as content delivery platforms, facilitating student interaction, fostering collaborative learning, and promoting question-and-answer practice, among other applications. Moreover, integrating chatbots into teaching practices empowers educators to analyze and assess students’ learning abilities and comprehension levels. However, much of the existing research on educational instruments, including chatbots, lacks both theoretical support from recent advancements in the learning sciences and an evidence-informed foundation for selecting appropriate data and information models. As a consequence, educational chatbots run the risk of yielding unintended negative consequences instead of delivering the intended benefits. This study seeks to address this gap by grounding the design of educational chatbots in the principles of learning sciences. We argue that effective communication through educational chatbots necessitates formulating information in the form of feedback dialogues to enhance learners’ comprehension. Additionally, we align the design of educational chatbots with learner-centric and mindful technology concepts, inline with Industry 5.0 digitization strategies.

PurposeThis research aims to explore digital feedback needs/preferences in online education during lockdown and the implications for post-pandemic education.Design/methodology/approachAn empirical study approach was used to explore feedback needs and experiences from educational institutions in the Netherlands and Germany (N = 247) using a survey method.FindingsThe results showed that instruments supporting features for effortless interactivity are among the highly preferred options for giving/receiving feedback in online/hybrid classrooms, which are in addition also opted for post-pandemic education. The analysis also showed that, when communicating feedback digitally, more inclusive formats are preferred, e.g. informing learners about how they perform compared to peers. The increased need for comparative performance-oriented feedback, however, may affect students' goal orientations. In general, the results of this study suggest that while interactivity features of online instruments are key to ensuring social presence when using digital forms of feedback, balancing online with offline approaches should be recommended.Originality/valueThis research contributes to the gap in the scientific literature on feedback digitalization. Most of the existing research are in the domain of automated feedback generated by various learning environments, while literature on digital feedback in online classrooms, e.g. empirical studies on preferences for typology, formats and communication channels for digital feedback, to the best of the authors’ knowledge is largely lacking. The findings and recommendations of this study extend their relevance to post-pandemic education for which hybrid classroom is opted among the highly preferred formats by survey respondents.

After some time of lockdown experiences, limited attention for feedback and the absence of feedback digitalization frameworks suggests rethinking traditional feedback practices toward post-pandemics digital/hybrid education. This research surveyed feedback digitalization needs in the context of online education in high education institutions in the Netherlands and Germany during the COVID-19 pandemic. The dimensions surveyed included preferences for feedback such as typology of feedback (e.g., cognitive, behavioral, etc.), formats (e.g., written, audio, video), online instruments, and features for communicating feedback. The results suggest that online instruments supporting features for effortless interactivity are among the highly preferred digital options for giving/receiving feedback. When given online, inclusive formats of feedback that inform learners not only about their own but also peer performance were also found to be among highly rated options. The increased need for inclusive feedback with peers, however, may also negatively affect students’ mastery orientations. Thus, balancing online with offline approaches should also be recommended when considering feedback digitalization approaches.

Student feedback analysis is time-consuming and laborious work if it is handled manually. This study explores the use of a new deep learning-based method to design a more accurate automated system for analysing students’ feedback ( called DTLP: deep learning and teaching process ). The DTLP employs convolutional neural networks (CNNs), bidirectional LSTM (BiLSTM), and attention mechanism. To the best of our knowledge, a deep learning-based method using a unified feature set, which is representative of word embedding, sentiment knowledge, sentiment shifter rules, linguistic and statistical knowledge, has not been thoroughly studied with regard to sentiment analysis of student feedback. Furthermore, DTLP uses multiple strategies to overcome the following drawbacks: contextual polarity; sentence types; words with similar semantic context but opposite sentiment polarity; word coverage limit of an individual lexicon; and word sense variations . To evaluate the DTLP, we conducted an experiment on a large volume of students’ feedback. The results showed ( i ) DTLP outperforms the existing systems in the field, ( ii ) DTLP that learns from this unified feature set can acquire significantly higher performance than one that learns from a feature subset, ( iii ) the ensemble of sentiment shifter rules, word embedding, statistical, linguistic, and sentiment knowledge allows DTLP to obtain significant performance, and ( iv ) an attention mechanism into CNN-BiLSTM improves the performance of DTLP. In addition, the deployed method looks for potential causes behind student feedback.

The significance of food is evident in the myriad challenges confronting contemporary society, including the increasing prevalence of diet-related diseases, food waste with its adverse economic, environmental, and social impacts, and the significant impact of food production on environmental issues, among others. As the negative health and environmental impacts of dietary patterns become more evident, there is a growing demand for personalized and sustainable food recommendations to promote healthier and planet-friendly choices. This study aims to enrich the theoretical underpinnings of food recommender systems with an emphasis on sustainable food consumption, by integrating insights from existing research, behavior change theories, and Industry 5.0 digitization concepts on humanity-centered technologies.

: Requirements analysis and modeling is a challenging task involving complex knowledge of the domain to be engineered, modeling notation, modelling knowledge, etc. When constructing architectural artefacts experts rely largely on the tacit knowledge that they have built based on previous experiences. Such implicit knowledge is difficult to teach to novices, and the cost of the gap between classroom knowledge and real business situations is thus reflected in further needs for post-graduate extensive trainings for novice and junior analysts. This research aims to explore the state-of-the art natural language processing techniques that can be adopted in the domain of requirements engineering to assist novices in their task of knowledge construction when learning requirements analysis and modeling. The outcome includes a method called Text-To-Model (TeToMo) that combines the state-of-the-art natural language processing approaches and techniques for identifying potential architecture element candidates out of textual descriptions ( business requirements ). A subsequent prototype is implemented that can assist a knowledge construction process through (semi-) automatic generation and validation of Unified Modeling Lnaguage (UML) models. In addition, to the best of our knowledge, a method that integrates machine learning based method has not been thoroughly studied for solving requirements analysis and modeling problem. The results of this study suggest that integrating machine learning methods, word embedding, heuristic rules, statistical and linguistic knowledge can result in increased number of automated detection of model constructs and thus also better semantic quality of outcome models.

Our earlier research attempts to close the gap between learning behavior analytics based dashboard feedback and learning theories by grounding the idea of dashboard feedback onto learning science concepts such as feedback, learning goals, (socio-/meta-) cognitive mechanisms underlying learning processes. This work extends the earlier research by proposing mechanisms for making those concepts and relationships measurable. The outcome is a complementary framework that allows identifying feedback needs and timing for their provision in a generic context that can be applied to a certain subject in a given LMS. The research serves as general guidelines for educators in designing educational dashboards, as well as a starting research platform in the direction of systematically matching learning sciences concepts with data and analytics concepts.

Technological advancements have generated a strong interest in exploring learner behavior data through learning analytics to provide both learner and instructor with process-oriented feedback in the form of dashboards. However, little is known about the typology of dashboard feedback relevant for different learning goals, learners and teachers. While most dashboards and the feedback that they give are based only on learner performance indicators, research shows that effective feedback needs also to be grounded in the regulatory mechanisms underlying learning processes and an awareness of the learner's learning goals. The design artefact presented in this article uses a conceptual model that visualizes the relationships between dashboard design and the learning sciences to provide cognitive and behavioral process-oriented feedback to learners and teachers to support regulation of learning. A practical case example is given that demonstrates how the ideas presented in the paper can be deployed in the context of a learning dashboard. The case example uses several analytics/visualization techniques based on empirical evidence from earlier research that successfully tested these techniques in various learning contexts.

In this paper, we advent a novel approach to foster exploration of recommendations: IntersectionExplorer, a scalable visualization that interleaves the output of several recommender engines with human-generated data, such as user bookmarks and tags, as a basis to increase exploration and thereby enhance the potential to find relevant items. We evaluated the viability of IntersectionExplorer in the context of conference paper recommendation, through three user studies performed in different settings to understand the usefulness of the tool for diverse audiences and scenarios. We analyzed several dimensions of user experience and other, more objective, measures of performance. Results indicate that users found IntersectionExplorer to be a relatively fast and effortless tool to navigate through conference papers. Objective measures of performance linked to interaction showed that users were not only interested in exploring combinations of machine-produced recommendations with bookmarks of users and tags, but also that this "augmentation" actually resulted in increased likelihood of finding relevant papers in explorations. Overall, the findings suggest the viability of IntersectionExplorer as an effective tool, and indicate that its multi-perspective approach to exploring recommendations has great promise as a way of addressing the complex human-recommender system interaction problem.

The road to publishing public streaming data on the Web is paved with trade-offs that determine its viability. The cost of unrestricted query answering on top of data streams, may not be affordable for all data publishers. Therefore, public streams need to be funded in a sustainable fashion to remain online. In this paper we present an overview of possible query answering features for live time series in the form of multidimensional interfaces. For example, from a live parking availability data stream, pre-calculated time constrained statistical indicators or geographically classified data can be provided to clients on demand. Furthermore, we demonstrate the initial developments of a Linked Time Series server that supports such features through an extensible modular architecture. Benchmarking the costs associated to each of these features allows to weigh the trade-offs inherent to publishing live time series and establishes the foundations to create a decentralized and sustainable ecosystem for live data streams on the Web.

This work has been supported by EC funds from CITADEL project - Empowering Citizens To Transform European Public Administrations (H2020-SC6-CULT-COOP-2016-2017, EC Grant Agreement 726755).