The development of “ABCD” technology (Artificial intelligence, Blockchain, Cloud computing, and Big data) has promoted the design and development of new and innovative technologies, which fundamentally revolutionized the ways organizations manage themselves. However, digital transformations have also brought in new challenges. Against the backdrop of growing internationalization, cross-border collaborations, and increasing global trade, it is of utmost importance to explore how technology renovates the intercultural dialogues, negotiations, trainings, and the subsequent knowledge transfer processes. Researchers have suggested that technology will play a vital role in facilitating the need to work with people from all around the world. And from the perspective of intercultural exchanges, and cross-border/cross-cultural collaborations,it is necessary to know how to navigate the intercultural challenges effectively. The emergence of social robots like the Furhat allows to design rich human-robot interactions including verbal- and non-verbal communication elements, which is promising to deliver artificial intelligence (AI) -based intercultural training capabilities. This research project has two objectives: first, this research will bring in the AI factor in human landscape to explore the actions and interactions of AI and intercultural environment in transition; second, this study aims to provide orientations and recommendations for implementing interactions with social robots to enable AI-based intercultural trainings. Therefore, design criteria for implementing effective verbal and nonverbal interactions are outlined for achieving high standards of intercultural interactions to provide better understanding and interpretations of the contexts in which certain intercultural actions are practiced.

Research into artificial intelligence is not a very young field; its precursors can be traced back as far as the 16th century. Today’s technical development, however, is virtually leaping forward, with new intelligent chat systems and social robots playing no small part in this. This is revolutionizing a wide range of scientific and social fields. The very large publication numbers in this field illustrate this as well. In order to keep track of the discourse in the field, the representatives of the field, the publications as well as the topics and their future development, it is indispensable for academics and scientists to prepare them in a bibliometric analysis. Only in this way it is possible to uncover thematic gaps as well as further points of contact and to drive research forward in a targeted and stringent manner. It is precisely this sorting and processing of the research discourse, the topics, and the authors, which is necessary for further research, that is carried out in this paper. For this purpose, using bibliometric analysis tools, an overview of the past, present, and future of the research field is created, and the general relevant topics are uncovered. The analysis includes as performance analysis a) the total number of publications and b) the total number of citations, and for science mapping c) a co-citation analysis (past), d) a bibliographic coupling (present) and e) a co-word analysis (future). The data needed for the analysis are identified and extracted from the SCOPUS or Web of Science (ISI) databases

Ongoing assessments in a course are crucial for tracking student performance and progress. However, generating and evaluating tests for each lesson and student can be time-consuming. Existing models for generating and evaluating question-answer pairs have had limited success. In recent years, large language models (LLMs) have become available as a service, offering more intelligent answering and evaluation capabilities. This research aims to leverage LLMs for generating questions, model answers, and evaluations while providing valuable feedback to students and decentralizing the dependency on faculty. Our approach is based on the development and interaction of advanced AI-powered generative agents, built on large language models like ChatGPT, GPT-4, and Vicuna, and designed to emulate human activities such as information abstraction, context refinement, and query rating. These agents interact autonomously in a network, employing techniques like Zero-Shot and Few-Shot Prompting to generate responses and adapt to various roles and contexts. The setup includes three key agents for question generation, refinement, and quality assurance, which leverage text vectorization, document selection and filtering, and cutting-edge language models to generate, refine, and evaluate questions and answers based on specific learning objectives. In conclusion, this paper demonstrates the versatility of LLMs for various learning tasks, including question generation, model answer generation, and evaluation, all while providing personalized feedback to students. By identifying and addressing knowledge gaps, LLMs can support continuous assessment and help students improve their understanding before semester exams.

The wave of digitization has begun. Organizations deal with huge amounts of data, such as logs, websites, and documents. A common way to make the information contained in these sources machine-accessible for automated processing is to first extract the information and then store it in a knowledge graph. A key task in this approach is to recognize entities. While common named entity recognition (NER) models work well for common entity types, they typically fail to recognize custom entities. Custom entity recognition requires data to be manually annotated and custom NER models to be trained. To efficiently extract the information, this paper proposes an innovative solution: Our Gazetteer approach uses a knowledge graph to create a coarse and fast NER component, reducing the need for manual annotation and saving human effort. Focusing on a university use case, our Gazetteer is integrated into a chatbot for entity recognition. In addition, data can be annotated using the Gazetteer and an NER model can be trained. Subsequently, the NER model can be used to recognize unseen custom entities, which are then added to the knowledge graph. This will improve the knowledge graph and make it self-extending.

Extracting triples of subjects, objects, and predicates from text to populate knowledge bases traditionally involves several intermediate steps such as co-reference resolution, named entity recognition, and relationship extraction. Treating triple extraction as translation task from source sentences to sets of triples, we present an end-to-end solution for information extraction that uses task prefixes to prompts a fine-tuned large language model to extract triples from text. Thus, the need for data labeling and training multiple models is reduced.

This paper presents the ongoing development of HAnS (Hochschul-Assistenz-System), an Intelligent Tutoring System (ITS) designed to support self-directed digital learning in higher education. Initiated by twelve collaborating German universities and research institutes, HAnS is developed 2021–2025 with the goal of utilizing artificial intelligence (AI) and Big Data in academic settings to enhance technology-based learning. The system employs AI for speech recognition and the indexing of existing learning resources, enabling users to search and compile these materials based on various parameters. Here, we provide an overview of the project, showcasing how iterative design and development processes contribute to innovative educational research in the evolving field of AI-based ITS in higher education. Notwithstanding the potential of HAnS, we also deliberate upon the challenges associated with ensuring a suitable dataset for training the AI, refining complex algorithms for personalization, and maintaining data privacy.

The following paper presents the evaluation of an artificially intelligent assistant system (DIAS) with a service-oriented chatbot as a central communication element. The conversational AI (Artificial Intelligence) is supposed to increase information transparency in higher education environments and thus support students, teachers, and administrative staff. The exploratory study had two objectives: first, we intended to find out about the usability and utility of the DIAS chatbot using the CUQ (Chatbot Usability Questionnaire) score and benchmark the results against other conversational agents. Secondly, we were interested in possible effects among the different variables of interest, which could contribute to further theory development of chatbots in education. The results show that the DIAS chatbot scored above average, and can support students in finding relevant information, particularly if they use the assistant frequently. Positive aspects included the intuitive use, a welcoming persona (expressed in design & language) and easy navigation. The negative feedback showed potential for improvement particularly in content quality and handling dialogue mistakes, which is a general shortcoming of conversational AI at this development stage. The results can be used as a guidance for future research and theory building. However, they must be considered carefully due to several study limitations.

In a current research project at the Ansbach University of Applied Science, an AI-based quiz function was created to serve as a voluntary student-oriented support offer to determine their learning progress in their respective courses by means of conducting self-assessment quizzes. The application takes lecture scripts as input and applies a question generation model to create questions that students can answer. In order to evaluate the given answers, another language model is involved to perform Natural Language Inference (NLI). Users can engage with the system via a graphical user interface currently provided via a web app. To assess preliminary feasibility and perception of the model prototype, a qualitative focus group discussion following a semi-structured interview guideline prepared by the research team according to similar studies in the education field (Sek et al. 2012) was conducted with five participants. A transcript of the discussion was prepared and analyzed using the qualitative content analysis method according to Kuckartz. Overall, the quiz function was well received by the participants of the focus group. However, the prototype still has potential when it comes to generating meaningful questions and transparently assigning categories to the given answers. Furthermore, the quiz parameters should be individually adjustable by users. In the following paper, the development of the service is illustrated by outlining the considerations for the application design and the training procedure of the language models. Afterwards, the design of the qualitative focus group is described including the presentation of the results.

Dieses essential gibt einen Überblick über verschiedene Go-to-Market-Strategien für zweiseitige digitale Plattform-Geschäftsmodelle. Zunächst liegt der Fokus auf den Strategien zur sequenziellen, simultanen und viralen Kundenakquise. Im zweiten Teil zeigt der Beitrag, mit welchen Faktoren man positive Interaktionen fördert, um eine kritische Masse an Nutzern zu gewinnen.

Ambidextrie im Innovationsmanagement bezieht sich auf die Fähigkeit eines Unternehmens, sowohl das Kerngeschäft zu stärken (Exploit) als auch mit neuen Angeboten in bestehenden oder neuen Märkten zu agieren (Explore). Dabei besteht die Herausforderung darin, beide Innovationsmodi in einer komplementären Art und Weise einzusetzen, anstatt sich auf einen der beiden Modi zu beschränken. Eine ambidextre Ausrichtung und die Fähigkeit, beide Innovationsmodi, Exploit und Explore, gleichzeitig und in dem Maß einzusetzen, das die Umstände erfordern, ermöglicht es Unternehmen, schnell auf Veränderungen in der Branche zu reagieren und sich langfristig im Markt zu behaupten. Für eine erfolgreiche Umsetzung von Ambidextrie im Innovationsmanagement spielen die Unternehmenskultur, der Führungsstil resp. das Führungsverständnis ebenso eine Rolle, wie Grundsatzentscheidungen in Bezug auf die Digitalisierung. Es ist entscheidend, dass Unternehmen die Potenziale von Ambidextrie erkennen und lernen, den Exploit- und Explore-Modus gezielt und optimal einzusetzen, um langfristig Wettbewerbsvorteile zu erzielen. Dieser Grundlagenartikel fokussiert insbesondere auf die im weiteren Heft weniger angesprochenen Themenbereiche. Er ordnet die Ambidextrie im Innovationsmanagement anhand der Themenfelder Kultur, Führung und Digitalisierung ein und liefert Argumente, weshalb die Akzeptanz und Nutzung von Ambidextrie zu Wettbewerbsvorteilen führen.

Entrepreneurship in the form of business foundations, especially start-ups, drives progress and innovation on many levels. They are currently one of the critical drivers of economic prosperity in our society. More and more economic policy measures are aimed at promoting just this. However, strong regional differences are observed in start-up activity. Thus, the support of start-ups and the development of so-called entrepreneurial ecosystems are also playing an increasingly important role in economic and regional policy. It is necessary to look at each region singularly, to identify particularities, and to determine the needs of regional actors to derive suitable measures for establishing such an ecosystem. This article examines an exemplary region for specific determinants by applying a start-up ecosystem model. Strengths, weaknesses, and potentials of the business location about its framework conditions for start-ups are identified in two group interviews (founders and supporting institutions). The results show that the numerous interactions of a start-up ecosystem are not sufficiently targeted and efficiently managed in the current structures and that relevant actors perceive the effectiveness of ecosystem elements and support measures in a differentiated manner. The study was able to provide insights for future start-up and innovation policy measures and interventions in the region studied and are thus intended to serve methodologically as a role model for the analysis of other regional start-up ecosystems.

Due to migration, terror-threats and the viral pandemic, various EU member states have re-established internal border control or even closed their borders. European Association for Biometrics (EAB), a non-profit organisation, solicited the views of its members on ways which biometric technologies and services may be used to help with re-establishing open borders within the Schengen area while at the same time mitigating any adverse effects. From the responses received, this position paper was composed to identify ideas to re-establish free travel between the member states in the Schengen area. The paper covers the contending needs for security, open borders and fundamental rights as well as legal constraints that any technological solution must consider. A range of specific technologies for direct biometric recognition alongside complementary measures are outlined. The interrelated issues of ethical and societal considerations are also highlighted. Provided a holistic approach is adopted, it may be possible to reach a more optimal trade-off with regards to open borders while maintaining a high-level of security and protection of fundamental rights. European Association for Biometrics and its members can play an important role in fostering a shared understanding of security and mobility challenges and their solutions.