AI.EDU Research Lab
AI.EDU is a CATALPA project.
Artificial intelligence that supports learners and teachers in processing and structuring study content - AI.EDU is researching how exactly this can be achieved. But in order for AI to help learners improve their skills in the first place, teaching and learning processes must first be decoded and described. Therefore, the project proceeds 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.
- Lars van Rijn (FeU)
- Silke Wrede (FeU)
- Dr. Xia Wang (DFKI)
- Alexander Zimmermann (DFKI)
October 2018 through September 2022
- 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
- Wang, X., Gulenman, T., Pinkwart, N., de Witt, C., Gloerfeld, C & Wrede, S. (2020) Automatic Assessment of Student Homework and Personalized Recommendation, 20th IEEE Intl. Conf. on Advanced Learning Technologies and Technology-enhanced Learning, Tartu, July 2020. Best Full Paper Award at the International Conference on Advanced Learning Technologies and Technology-enhanced Learning (ICALT2020 | Tartu, Estonia, July 6-9, 2020).
- Wang, X., Li, H., Zimmermann, A., Pinkwart, N., de Witt, C., Wrede, S. E., Baudach, B. & van Rijn, L. (2022). Knowledge based intelligent Quiz generator. The 16th IEEE International Conference on Semantic Computing (ICSC2022), Jan 26 – 28, 2022.
- Wrede, S. E., Gloerfeld, C., de Witt, C., & Wang, X. (2022). Künstliche Intelligenz und forschendes Lernen - ein ideales Paar im Hochschulstudium!? In T. Schmohl & A. Watanabe, Künstliche Intelligenz in der Hochschulbildung. Chancen und Grenzen des KI-gestützten Lernens und Lehrens. transcript.