AI.EDU Research Lab
The project is part of the research cluster D²L² "Digitalization, Diversity and Lifelong Learning - Consequences for Higher Education" at the FernUniversität in Hagen.
Project leads: Prof. Dr. Claudia de Witt (FernUniversität - Chair of Education Theory and Media Education) and Prof. Dr. Niels Pinkwart (DFKI, HU und Visiting Professor bei D²L²) - see also Cooperations
Project duration: October 2018 through September 2022
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.
- Wang, X., Gülenman, T., Pinkwart, N., de Witt, C. Gloerfeld, C. & Wrede, S. (2020). Automatic Assessment of Student Homework and Personalized Recommendation. ICALT 2020. DOI: 10.1109/ICALT49669.2020.00051. URL: https://ieeexplore.ieee.org/document/9155651
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), 34-45. DOI: 10.3991/ijai.v2i2.14763. URL: https://online-journals.org/index.php/i-jai/article/download/14763/7925