Artificial Intelligence (AI) is drastically transforming the world around us. Rather than replacing humans, hybrid intelligence combines human and machine intelligence to leverage each of their individual strengths. We summarize different requirements and approaches identified to achieve hybrid intelligence and focus on conversational AI to build a cognitive agent that supports knowledge management within an organization. The agent automatically extracts knowledge from artifacts provided or published by the us- ers. In addition, the knowledge base steadily grows while the agent talks to the users and the users provide feedback and the system is continuously learning to extract new types of entities and relations to answer more questions based on the knowledge graph and to access other sources of information. The first types of entities and relations extracted already support users in finding colleagues with relevant skills or inter- ests. Based on information provided by the agent, collaboration among employees and, thus, knowledge sharing and transfer is encouraged. The collaboration between the cognitive agent as an AI artifact and employees combined with a system that learns and adapts while in use stressing explainability and trust in its answers entails a step towards hybrid intelligence.

Digital technologies have become increasingly important for educational institutions since the Covid-19 pandemic. In this paper, we present an artificially intelligent assistant system that supports students and prospective students on different levels. In addition to an AI-based chatbot as the central communication element, the virtual guidance system includes planning, study analysis, and motivation applications. To evaluate how the assistant can best address students’ needs, a qualitative focus group study with eight current students was conducted in April 2022, involving first a user testing of the chatbot prototype and second an assessment of different concept sketches for the planner and motivator applications. Results from the user testing of the chatbot suggest the importance of a vivid persona and appealing design, accurate, guided, direct answering, and optional push messaging. In the second part concerning planner and motivator, the students expressed the wish to integrate predominantly functions, which help to prepare on time for exams and ideally bundle the applications on one platform to avoid switching between different platforms. Furthermore, participants voiced privacy concerns, as well as an increase in distraction and competitive pressure through gamification. The findings were used to further develop and refine the digital assistant before launch. They give detailed insight into why and how integrated, digital assistants can be successful in educational settings and can be used for future research in the emerging research field of AI in teaching and learning.

A successful study requires an efficient study organization. Not all students succeed in this, especially in distance learning scenarios. In the DIAS project, a digital intelligent assistant for studying and teaching is to be developed. The AI-based assistant will accompany students, motivate them and enable them to better organize and successfully complete their studies. It serves as a planner, communicator, analyzer and motivator. The assistant is being developed in close cooperation with all stakeholders and tested as a model in two degree courses at the University of Applied Sciences Ansbach. An app and an information terminal are to be implemented as exemplary output channels at Ansbach University of Applied Sciences.

In a current research project at a German university, a digital study assistant based on conversational AI is being developed. In this research project, four main application areas of the digital assistant are planned: communicator, for answering questions, conversation, and mentoring; a planner, for performing time and task management and course planning; a motivator, for actively managing learning success with gamification elements; and an analyzer, for processing necessary information about the student's study progress. In order to identify different application examples and possibilities for these four areas of the digital study assistant, the aim of this paper is to conduct a literature review. In addition to application examples and possibilities, implementation goals and the improvement of study results through such applications were researched.