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About the Symposium

Recent advances in algorithms and Large Language Models (LLMs) are changing how digital tools are used in practice. Rather than serving only as background infrastructure for computation and information storage, these systems are increasingly deployed as interactive components of work and learning. They generate text, propose solutions, summarize evidence, and can support or shape human judgment. This shift raises core questions for the social and behavioral sciences. How should cognitive labor be divided in hybrid human–AI workflows? When does reliance improve decision quality, and when does it degrade it? How should responsibility, accountability, and trust be understood when decisions are mediated by LLMs?

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The symposium aims to bring together researchers from the social, behavioral, and computational sciences working on these issues, with a particular focus on implications for education, the workplace, and everyday decision-making.

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The symposium will be organized as a single-track event to allow generous time for presentations, discussion, and informal exchange.

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Thanks to the financial support of the EUonAIR Alliance, registration is free, and we are arranging preferential accommodation rates with local hotels for symposium participants.​​​

Keynote Speakers

Submission & Registration

We welcome empirical and theoretical contributions, including work that documents effects and mechanisms in the following areas: â€‹â€‹â€‹

To submit an extended abstract or full paper, please use the form below.

Important Dates

Submission deadline

1 February 2026

Acceptance notice

15 February 2026

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Local Organizers: Pierette El Howeyek, Dejan Glavas, Alexandros Karakostas, Yann Truong

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For more information, contact us at: euonair@essca.fr

Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.

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