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Research Priority 3: AI Everywhere

Primary Contacts

  • Prof. Dr. Michael Corsten, Institute for Social Sciences
  • Prof. Dr. Ulrich Heid, Institute of Information Science and Natural Language Processing
  • Prof. Dr. Beatrix Kreß, Institute of Intercultural Communication
  • Prof. Dr. Julia Rieck, Institute for Business Administration and Information Systems
  • Prof. Dr. Klaus Schmid, Institute of Computer Science

Brief description

The rapid development of new AI and ML models and methods enables the use of large-scale data and information sources. AI-based applications now penetrate all areas of the economy and everyday life. These developments, in turn, are driving transformation processes in society that affect, for example, the discourse in digital public domains, as well as educational and information competences, and present new challenges for business and political decision-making.

In this Research Priority, three areas in particular are addressed in close collaboration, with ongoing coordination and joint reflection.

Design and development of AI and AI applications

This involves the development of AI and machine-learning models and methods, as well as the creation of (large-scale) AI systems, covering the entire processes from design through implementation to deployment (AI engineering). To improve the sustainability of AI methods and models, aspects of Green AI are integrated. The most important application areas include process optimisation and control using AI, mobility, Industry 4.0, Work 4.0, social services, and teaching and learning (AI applications in everyday life). The acceptance of AI systems requires the continuous development of AI-based interaction systems, including intuitive human–machine interfaces. In particular, this involves addressing questions of usability, user experience, and process design. AI alignment and AI safety deal with aligning AI with the values of a democratic society, as well as preventing misuse and harmful consequences that may arise from AI systems.

Analysis of transformations in society driven by AI and digitalisation

As a result of the influence of AI-based information aggregation and evaluation processes in digital public domains (e.g., platform architectures), changes are occurring in both expert and everyday discourses. These changes need to be examined using methods from communication studies and discourse analysis. An AI-shaped information market necessitates analytical and design-oriented research on information literacy, considering both professional and private users. In an educational landscape characterised by easier access to information and digital processing tools, it is necessary to examine the skills and abilities of people of all ages who operate in this new environment and, in particular, must filter and evaluate information (AI in and for education). In this context, the relationship between education and digital information competence also needs to be clarified. AI-related challenges for business and politics relate to the challenges facing corporate and political governance, as well as government action, arising from the availability and use of AI-based systems, as well as the question of whether—and how—these systems require regulation.

Development of interactive (hybrid) methodological combinations

In particular, hybrid (or synthetic) intelligence - as a combination of human and artificial intelligence in the sense of human–machine cooperation (rather than just interaction) - is worth mentioning in this context. Hybrid intelligence maybe used, for example, in intelligent tutoring systems and teaching/learning systems, in consulting, or in the service sector. Multimodal AI models can process multiple modalities simultaneously—such as text, images, sound, video, and tables—and establish implicit connections between them. Examples include visual storytelling, creative writing, improved translation, and accessible technologies. For all AI methods and combinations of methods developed, the quality of results must be assessed. This involves the use of quality indicators and measures, as well as evaluation approaches and statistical methods for AI systems and their outputs (quality assessment and evaluation).

Due to numerous interdependencies between technical implementation and social impact, decisions must also take into account the implications for other disciplines. Consequently, the topics mentioned can only be addressed effectively through interdisciplinary collaboration and by combining perspectives from computer science, information science, linguistics, and social sciences.

For specific research questions, there are already collaborations with research groups in linguistics, translation studies, or (social and organizational) pedagogy. In addition, areas of overlap with other Research Priorities at the University of Hildesheim can be identified. In particular, the topic “Teaching and learning with AI: opportunities and limitations” is being pursued together with Research Priority 1 “Education and Participation in Society”, and the topic “Artistic practices and everyday aesthetics of AI” together with Research Priority 2 “Aesthetic Practice”.

The interdisciplinary value of collaboration in the Research Priority “AI Everywhere” opens up opportunities to examine AI from the perspective of both developers and users in connection with processes of digital transformation. In doing so, approaches for combining analytical techniques from the humanities, cultural studies and the social sciences with AI-based methods can be developed and further advanced through reciprocal disciplinary reflection.

Participating institutes

  • Institute of Computer Science
  • Institute for Business Administration and Information Systems
  • Institute for Mathematics, Mathematics Education and Computer Science Education
  • Institute of Information Science and Natural Language Processing
  • Institute of Intercultural Communication
  • Institute for Social Sciences

Interdisciplinary units

  • Knowledge Lab: Information Literacy and Accessibility (KILA)
  • Center for Digital Change 
  • VWFS Data Analytics Research Center (DARC)
  • Social Data Science Lab
  • Labor für digitale Textpraxis (Laboratory for Digital Text Practice)
  • Reading and Writing Center

Selected external partners

  • ZDIN – Zentrum für digitale Innovationen Niedersachsen (Center for Digital Innovation Lower Saxony)
  • L3S Research Center – Hannover

Selected externally funded projects

  • AI Write: AI tools for writing (Bohle-Jurok, Jaki, Mandl)
  • BeSt F:IT (Knackstedt, Lange, Pitsoulis)
  • Data triangulation in translation process research: (Lapshinova-Koltunski et al.)
  • Digital C@mpus Le@rning (Girnat, Schmid, Menthe, Kreß, Bermeitinger)
  • DiHuTra: Differences in Human and Machine Translation (Lapshinova-Koltunski et al.)
  • DTCT: Detect then ACT (Jaki, Mandl, Schünemann, Heid)
  • EDIKILEX - Edition, künstliche Intelligenz und Lexikographie Interdisziplinäre Zugänge und digitale Methoden im Umgang mit frühneuhochdeutschen Texten (Heid, Mandl et al.)
  • EnerVation (Mandl, Womser-Hacker)
  • ENTEP: Enhancing Teaching Practice in Higher Education (Kreß, Schlickau)
  • GENIUS: Generative AI for the Software Development Life Cycle (Schmid et al.)
  • HaSeKI Hate Speech Erkennung (Mandl, Heid, Jaki, Schünemann)
  • HULLS Wissenschaftsraum: Hannover-Hildesheim Urban Living Lab for Sustainability (Knackstedt, Rieck, et al.)
  • IIP-Ecosphere (Schmidt-Thieme, Schmid)
  • ILCIS Information Literacy and Civil Society (Mandl, Griesbaum)
  • InnovationPlus (multiple projects): Heid, Knackstedt, Rieck, Girnat, Schünemann
  • KI-GesKom: Barrierefreie Gesundheitskommunikation (Maas, Lapshinova-Koltunski, et al.)
  • KISS-Pro (Fleckenstein, Horbach)
  • Digital Media, Generation, and Communicative Power (Corsten et al.)
  • Learning to Optimize (Rieck, Schmidt-Thieme)
  • Match'In (Schammann, Schmid, Bendel)
  • Multivariate smoothing equations with coefficients from the general linear group (Mentemeier et al.)
  • Orte, Menschen, Reflexionen – DDR-digital (Partetzke et al.)
  • PameWiko (Lindner-Bornemann, Schlickau, Bührig)
  • ProXLab and ProXHybrid (follow-up project) (Knackstedt, Rieck)
  • ReGaP Management, ReGap-PgE: "Technology and open innovation process" and "Platform goes Energy" (Schmidt et al.)
  • Rez@Kultur (Heid, Knackstedt, Graf, Reinwand-Weiß)
  • SOLDISK (Corsten, Heid, Kneuer, Schammann)
  • ViFaPi Visuelle Fachinformation: Automatische Bildverarbeitung für Pflanzen in historischen Drucken (Mandl et al.)

Selected publications

  • Ackermann, C.; Rieck, J. (2025): Multiple plan approach for a dynamic dial-a-ride problem. OR Spectrum.
  • Achilles, L.; Mandl, T.; Womser-Hacker, C. (2023) Social media usage and posting behavior in the context of eating disorders: A content analysis approach integrating topics, emotions, and images and the phenomenon of K-pop thinspiration. In: Proceedings of the 17th International Symposium on Information Science.
  • Ackermann, C.; Rieck, J. (2023) A novel repositioning approach and analysis for dynamic ride-hailing problems. EURO Journal on Transportation and Logistics, Vol. 12, 100109.
  • Baumgart, J.; Mandl, T.; Bohle-Jurok, U. (2023) An empirical analysis of the use of AI-based text tools in academic writing: Attitudes, usefulness and interactions. In: Proceedings of the Technology for Second Language Learning Conference (TSLL).
  • Bexte, M.; Laarmann-Quante, R.; Horbach, A.; Zesch, T. (2022) LeSpell: A multi-lingual benchmark corpus of spelling errors to develop spellchecking methods for learner language. In: Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC).
  • Breznau, N.; Rinke, E. M.; Wuttke, A.; …; Teltemann, J.; et al. (2022) Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. In: Proceedings of the National Academy of Sciences, Vol. 199 (44).
  • Chamurally, S.; Rieck, J. (2024) A practical and robust approach for solving the multi-compartment vehicle routing problem under demand uncertainty using machine learning. Networks 84, pp. 300–325.
  • Deilen, S.; Lapshinova-Koltunski, E.; Hernández Garrido, S.; Maaß, C. et al. (2024): Evaluation of intralingual machine translation for health communication. In: Proceedings of the 25th Annual Conference of the European Association for Machine Translation (EAMT), pp. 467–477.
  • Deilen, S.; Lapshinova-Koltunski, E.; Hernández Garrido, S.; Maaß, C. et al. (2024): Towards AI-supported health communication in plain language: Evaluating intralingual machine translation of medical texts. In: Proceedings of the Workshop on Patient-Oriented Language Processing (CL4Health), pp. 44–53.
  • Di Maria, M.; Schoormann, T.; Grisold, T.; Knackstedt, R. (2024). Discarding echoes of the past: A taxonomy for designing socio-technical unlearning artifacts. In: Proceedings of the 32nd European Conference on Information Systems (ECIS 2024).
  • Falkner, J. K.; Thyssens, D.; Bdeir, A.; Schmidt-Thieme, L. (2023) Learning to control local search for combinatorial optimization. In: Amini, M.-R.; Canu, S.; Fischer, A.; Guns, T.; Kralj Novak, P.; Tsoumakas, G. (eds.) Machine Learning and Knowledge Discovery in Databases, Springer Verlag, pp. 361–376.
  • Faubel, L.; Schmid, K. (2024): MLOps: A multiple case study in industry 4.0. In: IEEE Proceedings of the 29th International Conference on Emerging Technologies and Factory Automation.
  • Girnus, L.; Panreck, I. C.; Partetzke, M. (2025) Zwischen Technokratisierung und Demokratieanspruch. Zur Relevanz technisch-naturwissenschaftlichen Wissens in Politik und politischer Bildung, Springer Verlag.
  • Griesbaum, J. (2023) Informationskompetenz. In: Kuhlen, R.; Lewandowski, D.; Semar, W.; Womser-Hacker, C. (eds.) Grundlagen der Informationswissenschaft, De Gruyter Saur Verlag.
  • Groß, J.; Möller, A. (2023). Effect size estimation in linear mixed models. arXiv:2302.14580.
  • Josi, F.; Wartena, C.; Heid, U. (2022) Preparing legal documents for NLP analysis: Improving the classification of text elements by using page features. In: Proceedings of the 8th International Conference on Natural Language Processing (NATP 2022), pp. 17–29.
  • Klötergens, C.; Yalavarthi, V. K.; Stubbemann, M.; Schmidt-Thieme, L. (2024) Functional latent dynamics for irregularly sampled time series forecasting. In: Proceedings of the ECML PKDD 2024, Lecture Notes in Computer Science, Vol. 14944.
  • Knackstedt, R.; Sander, J.; Kolomitchouk, J. (2022) Kompetenzmodelle für den Digitalen Wandel: Orientierungshilfen und Anwendungsbeispiele, Springer Verlag.
  • Kneuer, M.; Roch, J.; Heid, U.; Kliche, F. (2025) Solidarity versus security? Tracing shifts in the citizens’ discourse during the ‘migration crisis’ in Germany. European Politics and Society, 1–24.
  • Kneuer, M.; Corsten, M.; Heid, U.; Schammann, H.; Kahle, P.; Wallaschek, S.; Ziegler, F. (2021) Claiming Solidarity. European Journal of Social Theory, Vol. 25 (3), pp. 366–385.
  • Knierim, A.; Achmann-Denkler, M.; Heid, U.; Wolff, C. (2024) Divergent discourses: A comparative examination of blackout Tuesday and #BlackLivesMatter on Instagram. In: Proceedings of CLIC-IT 2024, Tenth Italian Conference on Computational Linguistics.
  • Kreß, B.; Schweiger, K. (2021) Video presentations, video conferences, seminar discourse or oral student presentation? Are "traditional" academic genres changing or even disappearing? SHS Web of Conferences, Vol. 99, 01047.
  • Kröher, C.; Gerling, L. K.; Schmid, K. (2023) Control action types: Patterns of applied control for self-adaptive systems. In: Proceedings of the 18th International Symposium on Software Engineering for Adaptive and Self-Managing Systems.
  • Madhua, H.; Satapara, S.; Modha, S.; Mandl, T.; Majumder, P. (2023) Detecting offensive speech in conversational code-mixed dialogue on social media: A contextual data set and benchmark experiments. Expert Systems With Applications, Vol. 215, 119342.
  • Manakhimova, S.; Macketanz, V.; Avramidis, E.; Lapshinova-Koltunski, E. et al. (2024): Investigating the linguistic performance of large language models in machine translation. In: Proceedings of the 9th Conference on Machine Translation (WMT24), pp. 355–371.
  • Mentemeier, S.; Wintenberger, O. (2022) Asymptotic independence ex machina: Extreme value theory for the diagonal SRE model. Journal of Time Series Analysis, Vol. 43 (5), pp. 750–780.
  • Pancratz, N.; Fandrich, A.; Diethelm, I. (2023) Didaktische Strukturierung von Unterrichtsmaterialien zum Thema "Künstliche Intelligenz". In: Bliesmer, K.; Komorek, M. (eds.) Didaktische Rekonstruktion: Fachdidaktischer Ansatz für aktuelle Bildungsaufgaben, BIS-Verlag, pp. 84–96.
  • Popovic, M.; Lapshinova-Koltunski, E. (2024) Gender and bias in Amazon review translations: by humans, MT systems and ChatGPT. In Proceedings of the International Workshop on Gender-Inclusive Translation Technologies, pp. 22–30.
  • Raab, C., Riesch, F., Tonn, B., Barrett, B., Meißner, M., Balkenhol, N., & Isselstein, J (2020) Target‐oriented habitat and wildlife management: Estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data. Remote Sensing in Ecology and Conservation, Vol. 6 (3), pp. 381–398.
  • Salamut, C., Kohnert, I., Landwehr, N. et al. (2023) Deep learning object detection for image analysis of cherry fruit fly (Rhagoletis cerasi L.) on yellow sticky traps. Gesunde Pflanzen 75, 37–48.
  • Schäfer, J.; Heid, U.; Klinger, R. (2024) Hierarchical adversarial correction to mitigate identity term bias in toxicity detection. In: Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 35–51.
  • Schammann, H.; Gluns, D.; Heimann, C.; Müller, S.; Wittchen, T.; Younso, C.; Ziegler, F. (2021) Defining and transforming local migration policies: A conceptual approach backed by evidence from Germany. Journal of Ethnic and Migration Studies, Vol. 47 (13), 2897–2915.
  • Schmidt-Thieme, L.; Rieck, J. (2023) Dynamische Routenoptimierung mit Maschinellem Lernen. TI-Magazin, Verkehrslogistik: digital und nachhaltig, Vol. 1/23.
  • Schoormann, T.; Stadtländer, M.; Knackstedt, R. (2023) Act and reflect: Integrating reflection into design thinking. Journal of Management Information Systems, Vol. 40 (1), pp. 7–37.
  • Spoerr, D.; Pitsoulis, A. (2022) Effects of COVID-19 on the most important hotel attributes for German leisure travelers: An empirical investigation, Leisure/Loisir, Vol. 47 (2), pp. 281–306.
  • Tavakoli, H.; Alirezazadeh, P.; Hedayatipour, A.; Banijamali Nasib, A. H.; Landwehr, N. (2021) Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks. Computers and Electronics in Agriculture, Vol. 181, 105935.
  • Yalavarthi, V. K.; Madhusudhanan, K.; Scholz, R.; Ahmed, N.; Burchert, J.; Jawed, S.; Born, S.; Schmidt-Thieme, L. (2024) GraFITi: Graphs for forecasting irregularly sampled time seriesTripletformer for probabilistic interpolation of irregularly sampled time series. In Proceedings of the AAAI Conference on Artificial Intelligence, 16255–16263.