Research focus areas

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The research of the department focuses on three main areas. Some research work is also located at the intersection of two of these foci.

Research in this area aims to support the digital transformation of university and vocational education and training. In the past, for example, we worked with several training providers to develop a holistic approach to measuring the quality and models for designing high-quality IT-supported training courses. Various IT tools were also designed, implemented and evaluated for university teaching with the aim of increasing the learning success of the students. Topics on which we are currently researching particularly intensively are the individualization of learning processes through the use of IT, e.g. through Smart Personal Assistants (such as Alexa or Siri) and with the help of approaches such as scaffolding and technical developments **such as artificial intelligence (AI) and Natural Language Processing. Selected publications from this research focus:

  • Weber, F., Wambsganss, T., Rüttimann, D., and Söllner, M. 2021. “Pedagogical Agents for Interactive Learning: A Taxonomy of Conversational Agents in Education,” ICIS 2021 Proceedings. (
  • Winkler, R., Söllner, M., and Leimeister, J. M. 2021. “Enhancing Problem-Solving Skills with Smart Personal Assistant Technology,” Computers & Education (165), p. 104148. (
  • Wambsganss, T., Kueng, T., Söllner, M., and Leimeister, J. M. 2021. “ArgueTutor: An Adaptive Dialog-Based Learning System for Argumentation Skills,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Y. Kitamura, A. Quigley, K. Isbister, T. Igarashi, P. Bjørn, and S. Drucker (eds.), New York, NY, USA: ACM, pp. 1–13. ( *Honourable Mention Award*
  • Söllner, M., Bitzer, P., Janson, A., and Leimeister, J. M. 2018. “Process Is King: Evaluating the Performance of Technology-Mediated Learning in Vocational Software Training,” Journal of Information Technology (33:3), pp. 233–253. (

This research focus deals with two facets related to the trustworthy and user-centric design of information systems. On the one hand, we research the theoretical foundations of concepts such as trust in information systems and their effective use. On the other hand, we research how these theoretical insights can be translated into concise and useful design knowledge that supports developers in better designing information systems. In addition to aspects of trust and the effective use of information systems, we also deal with the translation of other theoretical or normative information into design knowledge. Selected publications from this research focus:

  • Söllner, M., Mishra, A. N., Becker, J.-M., and Leimeister, J. M. 2022. “Use IT Again? Dynamic Roles of Habit, Intention and Their Interaction on Continued System Use by Individuals in Utilitarian, Volitional Contexts,” European Journal of Information Systems (Online First), Taylor & Francis, pp. 1–17. (
  • Renner, M., Lins, S., Söllner, M., Thiebes, S., and Sunyaev, Ali, A. 2021. “Achieving Trustworthy Artificial Intelligence: Multi-Source Trust Transfer in Artificial Intelligence-Capable Technology,” ICIS 2021 Proceedings. (
  • Söllner, M., Benbasat, I., Gefen, D., Leimeister, J. M., and Pavlou, P. A. 2018. “Trust,” *In MIS Quarterly Research Curations, Ashley Bush and Arun Rai, Eds., Http://Misq.Org/Research-Curations* (Released: October 2016, Last Update: June 2018).
  • Söllner, M., Hoffmann, A., and Leimeister, J. M. 2016. “Why Different Trust Relationships Matter for Information Systems Users,” European Journal of Information Systems (25:3), pp. 274–287. (

This research focus is based on the observation that AI-based systems can already achieve superhuman performance in some areas (e.g., in games such as chess, go, and poker), but cannot yet match human performance in other areas (e.g., in mathematics research or the comprehension of written texts). Accordingly, it can be assumed that in the next few decades it will be a matter of better understanding the strengths and weaknesses of human and artificial intelligence. Based on this, hybrid intelligence systems can be developed that exploit synergies between the strengths of human and artificial intelligence. Selected publications from this research focus:

  • Söllner, M., Janson, A., Rietsche, R., and Thiel de Gafenco, M. 2021. "Individualisierung in Der Beruflichen Bildung Durch Hybrid Intelligence: Potentiale Und Grenzen," in Künstliche Intelligenz in Der Beruflichen Bildung, Zeitschrift Für Berufs- Und Wirtschaftspädagogik - Beihefte, S. Seufert, J. Guggemos, D. Ifenthaler, H. Ertl, and J. Seifried (eds.), Stuttgart: Franz Steiner Verlag, pp. 163-181.
  • Wambsganss, T., Niklaus, C., Cetto, M., Söllner, M., Handschuh, S., and Leimeister, J. M. 2020. "AL: An Adaptive Learning Support System for Argumentation Skills," in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, R. Bernhaupt, F. "Floyd" Mueller, D. Verweij, J. Andres, J. McGrenere, A. Cockburn, I. Avellino, A. Goguey, P. Bjørn, S. Zhao, B. P. Samson, and R. Kocielnik (eds.), New York, NY, USA: ACM, pp. 1-14.(
  • Dellermann, D., Ebel, P., Söllner, M., and Leimeister, J. M. 2019. "Hybrid Intelligence," Business & Information Systems Engineering (61:5), pp. 637-643.(
  • Rietsche, R., Duss, K., Persch, J. M., and Söllner, M. 2018. "Design and Evaluation of an IT-Based Formative Feedback Tool to Foster Student Performance," ICIS 2018 Proceedings.(