Our community, Grounding Human-Centred AI on Embodied Multimodal Interaction, has the objective to investigate the impact and effects of the rapid expansion of AI and language technology in everyday life, education, and scientific research. We want to study the extent of such impacts by building on our experience with linguistic and psycholinguistic research methodologies in the domain of human interaction and multimodality.
Connected Community Activities
Planned Education Activities
- Student exchanges
- Academic staff exchanges
- Admin staff exchanges
- Workshops to discuss and consult on harmonisation to enable us to plan joint programmes and teaching
- Promote each other’s online courses Plan collaborative applications under Erasmus
- Plan collaborative applications under Erasmus
Planned Research Activities
- Student exchanges
- Academic staff exchanges
- Workshops, meetings, and online discussions and consultations on each group’s methodologies and research activities
- Meetings on how to overcome obstacles in utilising each other resources and facilities
- Invite partners and their collaborators to research activities online like reading groups or special lectures
- Plan collaborative applications under Erasmus and other funding agencies
Activities
Human-centred AI, linguistics research, and psycholinguistic methodologies
8-10 December 2025
Please consult the three days agenda here: EUtopia: Human-centred AI – Programme
The workshop brings together students and researchers from linguistics, psycholinguistics, psychology, cognitive science, computer science, natural language processing (NLP), language technology, computer vision, cognitive computing, AI, philosophy, and related disciplines who are interested in how language is used, processed, and interpreted across diverse contexts and modalities.
We welcome contributions from participants who are interested in exploring how humans communicate using spoken, written, signed, visual, and multimodal signals—and how these behaviours can be modelled, predicted, and generated by computational systems. This includes work addressing foundational and philosophical questions about the nature of meaning, representation, intentionality, and interpretation; the relationship between linguistic form and conceptual structure; and the extent to which computational models can genuinely capture or approximate human communicative competence as interactive participants. Participants may also engage with issues concerning explanation and transparency in modelling, the epistemic status of data-driven approaches, and the ethical and societal implications of deploying systems that process or generate human-like language and multimodal behaviour.
Our aim is to foster dialogue between theoretical and experimental work in Linguistics and Psycholinguistics to inform practical developments in NLP and AI. In particular, we seek to examine how established theories, empirical findings, and methodological tools from the language sciences can be adapted, extended, or rethought to address the challenges emerging in contemporary computational and AI research. Conversely, we are also interested in how insights from AI and data-driven modelling can inform and inspire new questions in the study of human language and cognition.
By encouraging interdisciplinary exchange, the workshop aims to identify shared problems, develop integrative frameworks, and contribute to a deeper understanding of both human communicative behaviour and the computational systems designed to emulate or support it.
We plan a fully hybrid event to discuss research and funding opportunities in the EUtopia CC’s areas of interest and expand our group with new members.
Participants will be invited to present their research, discuss research and funding ideas with each other and coordinate potential future research projects. They will also be invited to contribute to a report, which we hope to be published as a position paper.
Community member
Lead: Eleni Gregoromichelaki
Email address: eleni.gregoromichelaki@gu.se
Eleni Gregoromichelaki is a Professor of Linguistics within the Linguistics, Logic, and Theory of Science unit of FLoV at the University of Gothenburg. She has a background in both in Linguistics and Computational Linguistics. She has an MSc in Computational Linguistics and PhD in Linguistics from King’s College London where she investigated formal and computational models of psycholinguistically realistic grammars. Currently, she is interested in the modelling of dialogue by researching how human conversation fits into more general processes of interaction in the natural world. Interaction from this perspective involves not only the verbal exchange of signals but also multimodal perception/action and, even more generally, theories of information in technology, biology, and physics. Recently, she has also been investigating the significance of the development of Large Language Models (LLMs) for theories of language and cognition.
Lead: Simon Dobnik
Email address: simon.dobnik@gu.se
Simon Dobnik is a Professor of Computational Linguistics at University of Gothenburg where he leads the Cognitive Systems research group at the Centre for Linguistic Theory and Studies in Probability (CLASP) and teaches within the Masters in Language Technology programme (MLT). He has experience with both textual (linguistic) and non-textual (images, robots) domains of natural language processing. His main research interests are computational representation of meaning (semantics), computational models of language and perception, human-robot interaction, and scenarios with low-resource data. In his research, he focuses on building, evaluating, and improving language models in a way that is informed by the properties of machine learning algorithms, representation of knowledge in the training data, and research in linguistics and psychology on human interaction. Examples of his work include learning language with robots, learning grounded language models, generation of image captions, (visual) question answering, natural language inference, learning language models from small and variable data, and evaluation of bias.