How can universities provide individual feedback and useful support systems to every student when class sizes continue to grow and learner needs become increasingly diverse? Artificial intelligence offers promising opportunities to provide personalized learning and teaching support on a larger scale. Yet as regulations regarding data protection, transparency and the responsible use of AI continue to evolve, institutions face a difficult balancing act between innovation and regulatory compliance.
These issues were addressed in the IMPACT project (Implementation of AI-based Feedback and Assessment with Trusted Learning Analytics in Higher Education Institutions), which explored how AI can support students and educators throughout the academic journey. Its goal was to develop AI-based methods for the semi-automated analysis of academic texts, to allow for more effective support in learning, teaching and assessment.
A key priority of the project was ensuring that AI could be integrated into higher education in a sustainable and responsible way. To achieve this, the project focused on open-source, interoperable solutions that could be embedded within existing university structures. These solutions were designed to support academic advising and student orientation, provide formative feedback during the learning process, and enhance summative assessment and feedback in examinations.
The project brought together Goethe University, Humboldt University Berlin, Hagen University, Freie Universität Berlin and University of Bremen in a collaborative effort to turn research into practice. Building on established work in learning analytics (LA), AI-supported feedback and trusted learning analytics, it drew on recognized frameworks such as the TLA Code of Conduct and the SHEILA process model to guide the responsible implementation of AI-driven learning analytics in higher education institutions.
For a more detailed look at the project, please read the newly published project evaluation report:
Drachsler, H., Seidenberg, N., Sacher, P., Gattinger, T. & Mateen, S. (2026). Implementierung von KI-basiertem Feedback und Assessment mit Trusted Learning Analytics in Hochschulen – IMPACT. online: Technische Informationsbibliothek (TIB). https://doi.org/10.34657/40458