DIAS - Digitaler intelligenter Assistent für Studium und Lehre

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.

Finding the required information to succeed in the organisation of everyday study life is not always easy for a student. Ontologies are an instrument to define a domain by illustrating its concepts and thereby presenting knowledge in a structured way. In this paper, our aim is to design an ontology that is suitable for the higher education environment of a German university to build a Knowledge Graph for a conversational AI. As a research context, the Ansbach University of Applied Science is used. The paper is organised into five sections. After a brief introduction in Section 1, Section 2 reviews previous work of conducted ontologies within the higher education environment, whereas Section 3 outlines the methodology for developing the ontology and presents the final result. The development procedure is thereby partly based on the ontology framework provided by Stanford University (Noy & McGuinness, 2001). The presented ontology, which delivers possible classes for the development, and transferability to other universities will then be discussed in Section 4. Finally, the conclusion and approaches for future work with ensuring a constant up-to-dateness of the classes are given in Section 5.

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.

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.

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.

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.