As generative AI and large language models reshape how we interact with chatbots, voice assistants and conversational agents, HUMIC focuses on the linguistic, social and relational dimensions of these technologies—areas often overlooked in technical development.
HUMIC, led by Dr. Doris Dippold and supported by the Surrey Institute for Advanced Studies, the BAAL Special Interest Group ‘Humans, Machines, Languages’ and the Surrey Institute for People-Centred AI, aims to foster interdisciplinary dialogue and connect academic insights with industry practice. The aim of the workshop is to advance conversational technologies that are context-aware, socially responsive, and cater for their users’ rapport needs.
KEYNOTE SPEAKERS
Maaike Groonewege, ConvoCat, Netherlands - Linguist and Conversation Designer
Bettina Migge, University College Dublin, Ireland - Language and AI Technology
Christian Hildebrand, University of St Gallen, Switzerland - AI and Language in Consumer Behaviour
ORGANISERS
Dr Doris Dippold, Literature and Languages
Dr Fabio Fasoli, School of Psyschology
Dr Di Fu, School of Psychology
Dr Richard Green, School of Health Sciences
Assistant Professor Amal Haddad, University of Granada
Professor Constantin Orasan, Literature and Languages
Dr Valentina Pitardi, Strategy, Marketing and International Business
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Registration details will be available soon.
Deadline for Abstracts: 16th March 2026
Notification of Outcome: 27th April 2026
As generative AI and large language models reshape how we interact with chatbots, voice assistants and conversational agents, HUMIC focuses on the linguistic, social and relational dimensions of these technologies—areas often overlooked in technical development.
HUMIC, led by Dr Doris Dippold and supported by the Surrey Institute for Advanced Studies, the BAAL Special Interest Group ‘Humans, Machines, Languages’ and the Surrey Institute for People-Centred AI, aims to foster interdisciplinary dialogue and connect academic insights with industry practice. Such insights are vital for developing conversational technologies that are context-aware, socially responsive, and cater for their users’ rapport needs.
We invite contributions that explore the complex interplay between humans and machines with reference to these factors. We welcome submissions from researchers working across the disciplines, for example but not limited to linguistics, psychology, sociology, natural language processing, UX research, and conversation design. Submissions may focus on any domain. We particularly welcome submissions from industry, focusing for example on common challenges and practices in designing conversational systems with linguistic, social and relational perspectives in focus.
During the workshop, participants will be invited to participate in a collaborative session. The session will encourage the generation of new research ideas and explore how research can respond to industry challenges. Selected works resulting from this workshop will be considered for a potential special issue.
We invite 300-word proposal on topics related to the workshop. Themes of interest include, but are not limited to:
- Linguistic and pragmatic dimensions of human-machine dialogue
- Social and relational dynamics in conversational AI, including rapport-building, empathy and trust
- Designing inclusive and accessible conversational systems that account for the needs of diverse users (linguistic, cultural, neurodiverse)
- Linguistic choices and their role in shaping user expectations, satisfaction, engagement and decision-making
- Evaluation methods and metrics for linguistic, social and relational outcomes in human–machine interaction (qualitative, quantitative, or mixed-methods)
- Model training and fine-tuning strategies for enhancing linguistic, social and relational outcomes in human–machine interaction.
- Interdisciplinary and academic-industry collaboration in the development of linguistically, socially and relationally aware conversational technologies
Accepted submissions will be assigned to oral or poster presentation formats according to the mode of presentation best suited to their content.
