Artificial Intelligence in Education at HFT Stuttgart

Overview

The focus of the KNIGHT project is, on the one hand, the individualisation of student learning processes and the support of professors in their supervisory tasks and, on the other hand, the development of competences that promote the trustworthy and competent use of AI technology.
The focus is on implementing existing and new AI approaches with potential for education within and outside HFT Stuttgart. Ethical guidelines ensure transparent processes and thus ensure the responsible handling of sensitive, personal data. The ethical guidelines are based on an ethical foundation of values. In addition, specific AI education programmes are to be established in AI-related technical but also non-technical disciplines. The activities and results will be transferred to economy and society via existing and new networks across universities.

Research questions

The project focuses on two topics. How can AI contribute firstly to supporting and assessing learning processes and secondly to supporting teaching activities?

Scientific approach and methods

Based on a literature review, qualitative research methods (focus groups, expert interviews) are used to reliably obtain more detailed data and information for the development of an interdisciplinary competence matrix in teaching and learning. The validation of the acquired AI competences will be carried out by means of Learning Analytics (LA). After analysing the respective needs, a "Digital Educational Mirror" for students, a "Digital Assistant for interaction analysis" in online meetings, a "Digital Educational Lecture Cockpit" for professors and a "Study Dean Cockpit" for deans of studies will be designed and prototypically implemented on the basis of an LA platform and other data systems.
For the use of performance-adaptive tests, AI-based methods are being developed for the (partially) automatic evaluation of student answers and for predicting the optimal level of difficulty for newly created questions. The developed tests will be tested in lectures.
Also on the basis of extensive literature analyses and the research design of transdisciplinary Living Labs underlying the entire project, an ethical value framework is being developed. The derivation of concrete ethical guidelines from the value framework is an iterative process.

Intended research results

  • an AI competence matrix as an orientation framework for the collection and visualisation of LA
  • ethical guidelines for the responsible use of AI at universities 
  • guidelines for data collection and analysis of personal (student) data based on ethical and data protection core requirements
  • possibilities for AI-based interaction analyses in digital spaces
  • opportunities and limits of reflecting learning success back to students through an LA platform with integrated AI tools
  • opportunities and limits of reflecting the students' learning success back to professors while complying with data protection law and ethical rules
  • competence-oriented teaching and learning opportunities that are individually adapted to the learning progress of the students

Furthermore, a series of concrete measures is being developed to sustainably strengthen AI competence at HFT Stuttgart.

ManagementProf. Dr. Dieter Uckelmann, Prof. Dr. Peter Heusch, Prof. Dr. Ulrike Padó, Prof. Dr. Tobias Popović, Prof. Dr. Alexander Rausch
E-Mailknight@hft-stuttgart.de
WebsiteKNIGHT (hft-stuttgart.de)
FundingFederal Ministry of Education and Research
ProgrammeKI in der Hochschulbildung (AI in higher education)
Call for proposalKünstliche Intelligenz (artificial intelligence)
Duration01.12.2021 - 31.08.2025

 

Team

Name & Position E-Mail & Telephone
Professor / Dekan+49 711 8926 2897 2/363
Professor+49 711 8926 2560 2/209
Professorin +49 711 8926 2811 2/449
Professor / Ethikbeauftragter der HFT Stuttgart+49 711 8926 2962 L 109
Professor für Wirtschaftsinformatik+49 711 8926 2513 2/360
Professor / Wissenschaftlicher Direktor+49 711 8926 2632 2/145
Referentin für Ethik+49 711 8926 2354 1/315
Akademische Mitarbeiterin
+49 711 8926 2307 2/446
Akademische Mitarbeiterin+49 711 8926 2995 2/549
Akademische Mitarbeiter- Machine Learning+49 711 8926 2772 2/221
344
+49 711 8926 2388 1/315