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.
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.
This project is a cooperation with Research Cente CATALPA – Center of Advanced Technology for Assisted Learning and Predictive Analytics, FernUniversität in Hagen.
Partners
CATALPA – Center of Advanced Technology for Assisted Learning and Predictive Analytics, FernUniversität in Hagen;
Lehrgebiet Bildungstheorie und Medienpädagogik, FernUniversität in Hagen
