Topic: AI

On 29 September, the University of Tartu Library’s conference centre hosted the competence day “AI at the university: skills, responsibility and critical choices”.

In September, the University of Tartu safe AI representative group (Tartu Ülikooli turvalise tehisaru esindusrühm or TÜTTE), made up of student volunteers, was established at the University of Tartu.

A working group at the University of Tartu Centre for Ethics, has completed an independent analysis, commissioned by the AI Leap Foundation, of the risks associated with the AI Leap educational programme and its AI-based learning application. The analysis describes the risks involved in using AI in education and assesses how AI Leap, the state, schools, research institutions and technology providers can mitigate them.

Djuddah Arthur Joost Leijen, Associate Professor of English Language and Head of the Centre for Academic Writing at the University of Tartu, has been selected to participate in a three-year research seminar organised by the Center for Engaged Learning at Elon University in the US.

The University of Tartu will welcome hundreds of students, researchers and academics from around the world in 2027 as it hosts the European Summer School in Logic, Language and Information (ESSLLI) for the first time in Estonia.

UT High Performance Computing Center offers AI-related services

On 19 June, the senate approved the objectives and principles for using artificial intelligence (AI), establishing a comprehensive framework for applying AI in teaching, research and support services at the university. The document defines the role of AI at the university, the objectives pursued through its use, and the principles that university members should observe when using AI.

Twelve interdisciplinary doctoral positions will be created at the University of Tartu to explore potential applications of artificial intelligence, including, for example, addressing mental health issues, reducing food waste, assessing the overall impact of spatial planning, and facilitating sign language interpreting.

Researchers showed that large language models can identify, with high accuracy, why patients stop using antidiabetic medications or statins, based on doctors’ electronic clinical notes.