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KI-basierte Sprachmodelle in der Lehre: Question-Generation-Modelle zur Messung des Lernfortschrittes von Studierenden

Woldai, Betiel ; Schacht, Sigurd; Kamath Barkur, Sudarshan (2024)

Neues Handbuch Hochschullehre - Sonderausgabe zur TURN23.


Open Access
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Magenta: Metrics and Evaluation Framework for Generative Agents based on LLMs

Kamath Barkur, Sudarshan ; Schacht, Sigurd (2024)

In: Tareq Ahram, Waldemar Karwowski, Dario Russo and Giuseppe Di Bucchianico (eds) Intelligent Human Systems Integration (IHSI 2024): Integrating People and Intelligent Systems. AHFE (2024) International Conference. AHFE Open Access, vol 119. AHFE International, USA..
DOI: 10.54941/ahfe1004478


Open Access
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The hate speech barometer template: A tutorial on design and setup

Sauer, Sebastian; Piazza, Alexander; Schacht, Sigurd (2023)

5th International Conference of Business Meets Technology 2023, Valencia, (to be published).



Generative Agents to Support Students Learning Progress

Schacht, Sigurd; Kamath Barkur, Sudarshan ; Lanquillon, Carsten (2023)

5. International Conference Business meets Technology 2023, Valencia (to be published).



A Qualitative Evaluation of an AI-Based Study Progress Forecast.

Kamath Barkur, Sudarshan ; Fersch, Mascha-Lea; Henne, Sophie ; Schacht, Sigurd...

In: Schlippe, T., Cheng, E.C.K., Wang, T. (eds) Artificial Intelligence in Education Technologies: New Development and Innovative Practices. AIET 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 190. Springer, Singapore .
DOI: 10.1007/978-981-99-7947-9_1


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Ontology Definition for University Knowledge Graph

Woldai, Betiel ; Kamath Barkur, Sudarshan ; Henne, Sophie ; Schacht, Sigurd...

The Barcelona Conference on Education 2023: Official Conference Proceedings.
DOI: 10.22492/issn.2435-9467.2023.69


Open Access
 

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.

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Knowledge Science - Grundlagen. Methoden der Künstlichen Intelligenz für die Wissensextraktion aus Texten.

Lanquillon, Carsten; Schacht, Sigurd (2023)

Knowledge Science, 1. Auflage Springer Vieweg 2023 2023.
DOI: 10.1007/978-3-658-41689-8


 

Knowledge Science beschäftigt sich mit Konzepten, Methoden und Prozessen zur systematischen Erzeugung, Extraktion, Speicherung und Bereitstellung von Wissen zur Lösung von Problemen und lässt sich somit dem Wissensmanagement zuordnen. Kognitive Assistenten sorgen dafür, das richtige Wissen zur richtigen Zeit in der richtigen Art und Weise seinen Anwendern und Anwenderinnen bereitzustellen. Damit dies gelingen kann, kommen inzwischen zahlreiche Methoden der Künstlichen Intelligenz (KI) zur Unterstützung unterschiedlicher Aufgaben des Wissensmanagements zum Einsatz.

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Knowledge Science - Fallstudien. Wie mit Künstlicher Intelligenz die Wissenssicherung und -nutzung im Unternehmen unterstützt wird

Lanquillon, Carsten; Schacht, Sigurd (2023)

Springer Vieweg Wiesbaden.
DOI: 10.1007/978-3-658-41155-8


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Trendermittlung mit der Unterstützung eines kognitiven Assistenten

Horbaschk, Nora; Schacht, Sigurd (2023)

Knowledge Science – Fallstudien: Wie mit Künstlicher Intelligenz die Wissenssicherung und -nutzung im Unternehmen unterstützt wird, Springer Vieweg, Wiesbaden, S. 149-175.
DOI: 10.1007/978-3-658-41155-8_7


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Kognitive Assistenzsysteme im Projektmanagement

Henne, Sophie ; Schacht, Sigurd (2023)

Knowledge Science - Fallstudien: Wie mit Künstlicher Intelligenz die Wissenssicherung und -nutzung im Unternehmen unterstützt wird, Springer Vieweg, Wiesbaden, S. 177-191.
DOI: 10.1007/978-3-658-41155-8_8


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Lernen wie ein Mensch: Konzept eines Assistenten mit Wissenserwerb durch Beobachtung, Instruktion und Interaktion

Dreßler, Daniel; Schacht, Sigurd; Lanquillon, Carsten (2023)

Knowledge Science – Fallstudien: Wie mit Künstlicher Intelligenz die Wissenssicherung und -nutzung im Unternehmen unterstützt wird, Springer Vieweg, Wiesbaden, S. 69-107.
DOI: 10.1007/978-3-658-41155-8_5


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Generative Agents to Support Students Learning Progress

Schacht, Sigurd; Kamath Barkur, Sudarshan ; Lanquillon, Carsten (2023)

In Proc.: 5th International Conference Business Meets Technology. Valencia, 13th-15th July 2023. 179-197. .
DOI: 10.4995/BMT2023.2023.16750


Open Access
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Intro zu ChatGPT & Co - ein Experiment vom AN[ki]T "AI talks to AI"

Schacht, Sigurd; Piazza, Alexander (2023)

KI-Stammtisch des KI-Hub Bayern am Nürnberg DIGITAL FESTIVAL.



Knowledge-Grounded and Self-Extending NER

Kamath Barkur, Sudarshan ; Schacht, Sigurd; Lanquillon, Carsten (2023)

In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1836. .
DOI: 10.1007/978-3-031-36004-6_60


 

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.

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PromptIE - Information Extraction with Prompt-Engineering and Large Language Models

Schacht, Sigurd; Kamath Barkur, Sudarshan ; Lanquillon, Carsten (2023)

In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1836. 1836.
DOI: 10.1007/978-3-031-36004-6_69


 

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.

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Exploring AI in Education: A Quantitative Study of a Service-Oriented University Chatbot

Fersch, Mascha-Lea; Schacht, Sigurd; Woldai, Betiel (2023)

The Paris Conference on Education 2023: Official Conference Proceedings .
DOI: 10.22492/issn.2758-0962.2023.37


Open Access
 

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.

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A Qualitative Evaluation of an AI-supported Quiz Application to Assess Learning Progress

Woldai, Betiel ; Henne, Sophie ; Fersch, Mascha-Lea; Kamath Barkur, Sudarshan ...

The Paris Conference on Education 2023: Official Conference Proceedings, Vortrag am 18.06.2023.
DOI: 10.22492/issn.2758-0962.2023.39


Open Access
 

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.

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Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle

Mehlin, Vanessa ; Schacht, Sigurd; Lanquillon, Carsten (2023)

arXiv:2303.01980 [cs.LG].
DOI: 10.48550/arXiv.2303.01980


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Wissenssicherung mit Wissensgraphen

Lanquillon, Carsten; Schacht, Sigurd (2023)

DASC-PM v1.1 Fallstudien, S. 6-15.


Open Access
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Retaining Knowledge with Knowledge Graphs

Lanquillon, Carsten; Schacht, Sigurd (2023)

DASC-PM v1.1 Case Studies , S. 6-14.


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