Open Access

Internationalization of higher education expands opportunity but poses concrete pedagogical challenges for culturally, linguistically, and educationally diverse cohorts. This study reports a qualitative inquiry with 19 experienced faculty at a German University of Applied Sciences. Participants taught across engineering and business programs serving international cohorts. Using semi-structured interviews and thematic analysis, it identifies key challenges and field-tested practices for teaching in international programs. The analysis yields a three-pillar framework: (1) building an inclusive foundation through proactive communication and relationship-oriented pedagogy; (2) scaffolding academic success via explicit, responsive teaching and assessment; and (3) leveraging educational technology to provide flexibility and universal access. The framework reflects a facultydriven enactment of principles from Culturally Responsive Pedagogy (CRP) and Universal Design for Learning (UDL). The contribution is a theoretically grounded, practitioner-derived model that offers actionable guidance for instructors and program leads seeking inclusive, effective international education, and informs course design, assessment, and student support in globally diverse classrooms.

Readiness is a critical yet conceptually fragmented construct in Information Systems and adjacent disciplines. While existing frameworks often focus on technological, organizational, or environmental factors, they frequently overlook ethical, societal, and risk-oriented dimensions or conflate individual, organizational, and systemic levels of analysis. This study introduces a three-layer taxonomy of readiness that consolidates 14 core characteristics across six thematic dimensions. Each characteristic is classified by facet (what aspect of the system is involved), level (who is the subject of readiness), and phase (when readiness becomes relevant in a transformation process). The taxonomy was developed through a structured review of 20 highly cited cross-disciplinary articles researching “readiness” and validated by applying it to five Information Systems studies addressing readiness in the context of digital transformation, artificial intelligence adoption, and sustainability. The analysis revealed both dominant patterns - such as the emphasis on technological and structural readiness - and underrepresented areas, particularly regarding societal alignment and long-term adaptation. The taxonomy enables the identification of distinct readiness “signatures” and provides a structured lens for diagnosing readiness profiles across diverse contexts. By offering a unified conceptual framework, this taxonomy supports comparative analysis, reveals blind spots in current research, and lays the groundwork for theory-driven assessment tools. It contributes to readiness scholarship by bridging fragmented perspectives and enabling more precise inquiry into complex socio-technical change processes.

Viele deutsche Großunternehmen experimentieren derzeit intensiv mit künstlicher Intelligenz (KI), stehen aber vor der Frage, wie sich erste Pilotprojekte in einen nachhaltigen, wirtschaftlich wirksamen Einsatz überführen lassen. Eine empirische Studie mit 34 Chief Information Officers (CIO) und IT-Entscheidern in deutschen Großunternehmen zeigt: 112 identifizierte KI-Use-Cases, ein klar erkennbarer Reifezuwachs – aber auch deutliche Hürden bei Daten, Kompetenzen und Akzeptanz. Der Beitrag fasst den Status quo zusammen, validiert zentrale Erfolgsfaktoren aus der Forschung und leitet konkrete Empfehlungen für die Praxis von IT- und Fachbereichsverantwortlichen ab.

Generative Artificial Intelligence (GenAI) is transforming industries at an unprecedented rate, offering novel opportunities for productivity and innovation. This article explores the adoption of GenAI, highlighting its accelerated uptake compared to previous technologies. Key topics include productivity gains through GenAI tools (e.g., ChatGPT, Klarna’s AI assistant), challenges such as data quality and organizational readiness, and the implications for business strategy. Practical recommendations for managing generative AI adoption and maximizing its impact on both individual and organizational levels are also provided. The analysis underscores the necessity of aligning AI capabilities with customer needs and creating data-driven, adaptable business models.

Systematic literature reviews (SLRs) are foundational for research but resource-intensive to conduct. With the rise of large language models (LLMs) such as ChatGPT, generative AI (GenAI) tools are being increasingly explored for their potential to support and transform the SLR process. This study presents a systematic review of peerreviewed articles that examine how LLM-based GenAI tools are used in different SLR phases. Following the PRISMA 2020 guidelines, we screened 1,846 publications published since January 2021 until April 2025 and selected 54 for in-depth analysis. Each study was coded by review phase, prompting approach, automation level, validation type and challenges. Our findings show that GenAI is most often used to support in the screening, search, and writing phases, typically through Basic Prompting and under human oversight. While many studies report efficiency gains, concerns remain regarding validity, transparency, and methodological rigor. Moreover, GenAI is frequently applied to isolated tasks but is rarely embedded in a structured, methodologically guided review processhighlighting the need for clearer phase-specific guidance and standards. We offer a structured, phase-specific synthesis that highlights both the promise and the current limitations of GenAI in literature reviews and thereby offer practical recommendations for the responsible use of GenAI in literature reviews.