Background: Integrating artificial intelligence (AI) systems into nursing care often encounters obstacles stemming from unmet requirements and insufficient engagement with well-documented sociotechnical pitfalls. Readiness models offer a systematic way to evaluate project preparedness and to build the capabilities needed for successful artificial intelligence in nursing care (AINC) research, development, and implementation. As of yet, an evidence-based AI readiness assessment prioritizing AINC projects and accounting for their diversity in care settings is missing. Objective: This study aimed to develop a comprehensive artificial intelligence nursing care readiness assessment (AINCRA) to support planning, execution, and evaluation of AINC projects. Methods: In a sequential exploratory multimethods bottom-up approach to maturity model development, key AI readiness dimensions and attributes were identified to develop a pilot readiness assessment. The pilot version was grounded on insights from an expert workshop (n=21) and expert interviews (n=14), an online survey (n=53), a rapid review (n=292), and a nominal group consensus process. A systematic literature review (n=7) further triangulated AI readiness attributes. Finally, a think-aloud interview study and focus group discussions involving experts (n=18) from nursing practice, nursing science, and AI research and development who had conducted AINC projects prior to data collection validated the attributes. Results: The resulting AINCRA encompasses 5 core dimensions: regulatory, processual, technical, social, ethical, and community building requirements and aspects. Including 69 attributes and capabilities of AI nursing care readiness, the core dimensions reflect key areas of action where AINC project stakeholders can influence project outcomes. Clinical partners can assess their organization's maturity level in relation to the implementation of AI. An assessment of each dimension and its attributes across 5 maturity levels allows reflecting on and proactively shaping individual project approaches. Overall, experts regarded AINCRA as a useful instrument for the development, management, and evaluation of AINC projects while emphasizing that established principles of good practice in project and data management should not be neglected when using AINCRA as a project management tool. Conclusions: AINCRA enables practitioners from AI research and development, clinical partners, and nursing and health scientists to plan, evaluate, and enhance AI projects across their lifecycle, thereby supporting effective AI integration in nursing care. While AINCRA was developed within the European and German legal framework for AI in health care settings, respective attributes can be adapted to international requirements.

As established routines and other forms of knowledge may prevent organizations to respond to changing situations, they need to learn how to break out of the old way of thinking and acting. While unlearning is among the most promising approaches to do so, there is only a limited understanding of the requirements for the effective design of unlearning support systems (USS). As part of a larger design research project, this research-in-progress paper reviews 41 articles on practical approaches for unlearning support and derives an initial catalog of 23 design requirements. Our work aims to guide designers in the implementation and operationalization of unlearning support.

BACKGROUND:As populations age, informal caregivers play an increasingly vital role in long-term care, with 80% of care provided by family members in Europe. However, many individuals do not immediately recognize themselves as caregivers, especially in the early stages. This lack of awareness can increase physical and emotional stress and delay access to support services. The phenomenon of hidden care, where substantial care is provided without formally acknowledging the role, further exacerbates these issues. To address this, we developed an AI-driven chatbot designed to support informal caregivers recognize their role, reflect on their situation, and identify relevant support options. This paper explores how an AI-based chatbot can be designed to support informal caregivers in reflecting on and re-evaluating their caregiving roles. METHODS:Following a design science research approach, we evaluate the chatbot design via focused semistructured interviews and think-aloud sessions with informal caregivers to assess its utility, completeness and potential for supporting role transitions through the lens of unlearning. The data were analyzed via Braun and Clarke's thematic analysis. RESULTS:The chatbot has the potential to support caregivers in recognizing their role and reflecting on their experiences, with participants reporting increased self-awareness triggered by reflective prompts and recommendations of useful personalized support resources. Seven initial design principles for AI-based chatbot development in transitional informal care contexts were identified. These principles emphasize personalized assessment, transparent information, role awareness support, accessibility, and continuous companionship. CONCLUSIONS:This study demonstrates the potential of AI-driven chatbots to support informal caregivers during critical role transitions. Future research should build on these insights to design context-aware solutions that responsibly embed AI into caregiving realities. CLINICAL TRIAL:No clinical trial.

Zusammenfassung Die schnelllebige Entwicklung und Anwendung neuer digitaler Technologien führt zu einem erhöhten Innovationsdruck. Organisationen müssen sich an stetig verändernde Marktbedingungen anpassen und neue Ideen entwickeln, um Prozesse, Produkte und Geschäftsmodelle kontinuierlich zu erneuern. Für die Umsetzung solcher Ideen ist die Integration von Wissen über Disziplins- und Institutionsgrenzen hinaus gefordert. Da IT-gestützte Werkzeuge neue Möglichkeiten zur Unterstützung solcher Innovationsprozesse eröffnen, berichtet der vorliegende Beitrag über einen virtuellen Ideenwettbewerb – eine Design-Challenge – zum Thema ‚Einkaufserlebnis der Zukunft in Innenstädten‘. Dabei werden Erfahrungen aus der Vorbereitung und Durchführung synthetisiert. Der Wettbewerb wurde von einem Konsortium, bestehend aus einem universitären Forschungsprojekt, einem mittelständischen Unternehmen, einem regionalen Digitalisierungsinkubator, einer Großstadt und einem Start-Up, durchgeführt. Die teilnehmenden Teams bestanden aus Studierenden. Basierend auf den Erfahrungen und den kritischen Reflexionen aus der Vorbereitung und Durchführung des Wettbewerbs werden Handlungsempfehlungen für zukünftige virtuelle Design Challenges formuliert. Diese geben Unternehmen und öffentlichen Einrichtungen eine Orientierung hinsichtlich der Planung, Durchführung und Evaluation solcher Wettbewerbe.

: In diesem Beitrag stellen wir die Webanwendung COREFLECTOR zur Unterst(cid:252)tzung von Studierenden beim analytischen Lesen vor. Sie unterst(cid:252)tzt das Verlernen hinderlicher Verhaltensmuster beim Lesen und Verstehen wissenschaftlicher Texte, z. B. von vorne bis hinten durchlesen. Studierende identifizieren dabei Schw(cid:228)chen in ihrem bestehenden Leseverhalten, lernen eine neue Technik f(cid:252)r das analytische Lesen kennen und reflektieren eigene (Ver-)Lernerfahrungen und die anderer Studierender. Erste explorative Anwendungen deuten darauf hin, dass Studierende durch gemeinsames Reflektieren und Experimentieren eher dazu neigen, sich mit eigenen Verst(cid:228)ndnisproblemen und Techniken des analytischen Lesens auseinanderzusetzen.

: The rapid growth and obsolescence of knowledge cause uncertainty for university actors, such as students, teachers, administrative staff, and technology vendors. They must find new ways of dealing with hindering assumptions, and behaviors toward learning and teaching. To overcome these and achieve educational goals, it is essential to question ineffective ways of teaching and learning, adopt new education processes, and discard inadequate beliefs and procedures. This process is known as unlearning. In this position paper, we explore the potential of unlearning for mitigating educational challenges through unlearning. From a socio-technical perspective, we highlight the value of unlearning as a tool for tackling different challenges in university contexts. Finally, we identify central problem spaces stimulating further discussions.

Online-Rezensionen zu künstlerischen Artefakten können Bildungsprozesse anstoßen. Sowohl in der produktiven Auseinandersetzung mit einem Werk als auch in der Aufbereitung dieser Erfahrung in einem rezensiven Text und für ein spezifisches Publikum liegt ein hohes Potenzial hinsichtlich der kulturellen Teilhabe und Überwindung von Bildungsbarrieren. Aber welche Prozesse, Inhalte und Kontexte spielen dabei eine Rolle? Dieser Frage widmete sich das interdisziplinäre Forschungsprojekt Rez@Kultur, dessen Ergebnisse hier erstmals umfassend dargestellt werden. Ergänzt werden die Befunde um Anschlussperspektiven und Kommentare aus Forschung und Praxis.