AI.EDU Research Lab 2.0

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AI.EDU Research Lab 2.0 is an interdisciplinary CATALPA project

When searching for a term paper topic in the Bachelor's program, many students need assistance. Thereby stands out that recurring questions and support needs arise in every semester; the feedback from the lecturers is thus also repetitive. In the AI.EDU Research Lab 2.0, researchers are investigating how students' competencies can be strengthened through AI in such a way that they find a suitable subject-related, interest-driven topic. For the teachers, this should leave more room for research-promoting, stimulating interaction in 1:1 supervision.

Project goals and research questions

The AI.EDU Research Lab is exploring the use of AI in university teaching. In version 2.0, the research focuses on supporting students' competencies, especially in deriving a term paper topic and a related guiding question with recommender systems (RecSys) as well as with generative AI tools. For this purpose, the project is based on the results and experiences of the first research funding.

RecSys, based on different recommender methods, are used as a context-bound combination of AI technologies and didactic design for the purpose of transmitting recommendations to educational stakeholders. In the project, they are used to research and evaluate suitable AI methods to support students in finding a topic and generating a guiding research question for their term paper. A central research topic is, among other things, the transparency and trustworthiness of self-developed AI systems and those already in use. Comparatively, current tools and tasks for innovative applications with generative AI are explored.

  • Prof. Dr. Claudia de Witt (FernUniversität - Chair of Education Theory and Media Education) and Prof. Dr. Niels Pinkwart (DFKI, HU und Visiting Professor bei CATALPA) - see also Cooperations

  • October 2022 through September 2025

  • 2024

    Chapters in Edited Books

    • de Witt, C. (2024). Hochschuldidaktik mit hybrider Intelligenz: Unterstützung personalisierten Lernens. In U. Dittler & C. Kreidl (Eds.), Künstliche Intelligenz in der Hochschullehre: Einsatzmöglichkeiten und Entwicklungen digitaler Technologien im Hochschulalltag (pp. 249–264). Schäffer-Poeschel Verlag.

    2023

    Conferences

    Books

    Chapters in Edited Books

    • Wrede, S. E., Gloerfeld, C., & de Witt, C. (2023). KI und didaktik – zur qualität von feedback durch recommendersysteme. In C. de Witt, C. Gloerfeld, & S. E. Wrede (Eds.), Künstliche intelligenz in der bildung (pp. 133–154). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-40079-8{\textunderscore }7
    • Wrede, S. E., Gloerfeld, C., de Witt, C., & Wang, X. (2023). Künstliche intelligenz und forschendes lernen - ein ideales paar im hochschulstudium!? In T. Schmohl & A. Watanabe (Eds.), Künstliche intelligenz in der hochschulbildung. transcript.

    2022

    Conferences

    • Wang, X., Li, H., Zimmermann, A., Pinkwart, Niels., Wrede, S., van Rijn, L., de Witt, C., & Baudach, B. (2022). IFSE - personalized quiz generator and intelligent knowledge recommendation. 2022 IEEE 16th International Conference on Semantic Computing (ICSC), 201–208. https://doi.org/10.1109/ICSC52841.2022.00041

    Talks and Poster Presentations

    • Karolyi, H., & Wrede, S. (2022). Gestaltung formativer Feedbacks an Hochschulen mit Künstlicher Intelligenz und Trusted Learning Analytics [Presentation].

    2020

    Journals

    • Gloerfeld, C., Wrede, S., de Witt, C., & Wang, X. (2020). Recommender – potentials and limitations for self-study in higher education from an educational science perspective. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), 2(2), 34. https://doi.org/10.3991/ijai.v2i2.14763

    Conferences

    • Wang, X., Gülenman, T., Pinkwart, N., de Witt, C., Gloerfeld, C., & Wrede, S. (2020). Automatic assessment of student homework and personalized recommendation. In M. Chang (Ed.), IEEE 20th international conference on advanced learning technologies (pp. 150–154). IEEE. https://doi.org/10.1109/ICALT49669.2020.00051

    Other Publications

AI.EDU Research Lab 10/2018 – 09/2022

mehr Infos

AI.EDU was a CATALPA project.

Artificial intelligence that supports learners and teachers in processing and structuring study content - AI.EDU has researched how exactly this can be achieved. In order for learners to be supported by AI to improve their skills in the first place, teaching and learning processes first had to be decoded and described. The project proceeded in three phases from research to implementation and scaling.

Project goals and research questions

Although there has been relatively little research on artificial intelligence in higher education so far, the possibility raises high expectations for improvements in teaching and learning quality. In this cooperative project, Prof. Dr. Claudia de Witt’s Chair of Education Theory and Media Education, together with the German Research Center for Artificial Intelligence’s Educational Technology Lab, directed by Prof. Dr. Niels Pinkwart, jointly research methods and applications for artificial intelligence in teaching, learning and continuing education at the FernUniversität. The project will develop both scenarios which assist students with working through and structuring the course contents as well as applications which support students throughout the entire study program, and then initially test them in testbeds. The implementation focuses on knowledge-based expert systems, education data mining, and machine learning processes. One key goal of the three-year project is for these methods to support students both in training their metacognitive skills as well as with working through the course content using recommendation systems. In order to do this, teaching and learning processes will be decoded and clearly described.

The course of the project can be divided into three phases. In the first phase, Research, concepts and prototypes will be developed. In the second phase, Implementation, the concepts and their implementation will be tested and validated. Finally, in the third phase, Expansion, successful approaches will be broadly implemented and transferred to other usage scenarios. Ultimately, however, the project also focuses on considering the implications for education, and for future generations’ judgment and sense of responsibility in the design of algorithmic teaching and learning processes.

Project lead

Prof. Dr. Claudia de Witt (FernUniversität - Chair of Education Theory and Media Education) and Prof. Dr. Niels Pinkwart (DFKI, HU und Visiting Professor bei CATALPA) - see also Cooperations

Team

Project period

October 2018 through September 2022

Research Center CATALPA | 12.12.2024